JP2014525078A - Method and apparatus for reserving computing resources in a data processing system, method and apparatus for monitoring or managing the use of computing resources against a computing environment entitlement contract, and computing environment entitlement contract Method and apparatus for migration, and computer program related thereto - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a mechanism for reserving computing resources of a data processing system.
These mechanisms generate one or more computing environment entitlement agreement (CEEC) data structures, each of which is a business level between a contracting party and a data processing system provider. Define the terms of the agreement. These mechanisms associate a set of computing resources with a CEEC data structure. These mechanisms manage a set of one or more computing resources according to the associated CEEC. Such management involves contracting parties using computing resources within a selected computing resource cohort for a specified purpose at approximately the specified level and pattern strength for a specified period of time. All of which are identified in the CEEC data structure, including responding to failure, after which the CEEC data structure expires or becomes invalid.
[Selection] Figure 3

Description

  TECHNICAL FIELD This application relates generally to improved data processing apparatus and methods, and more particularly to managing an organization's computing resources in accordance with established computing environment entitlement contracts. It is related to the mechanism.

  Investment in computing resources represents an important overhead for large organizations in modern society. Large organizations such as the U.S. federal government, individual government departments and agencies, companies such as International Business Machines in Armonk, NY, Bank of America, General Electronic, Citigroup, and many others, You can have thousands of computers of various types, configurations, functions, and efficiency levels. It is a difficult task to manage all of these computing resources so that these organizations make efficient and optimal use of these computing resources. Efficient and optimal utilization of these computing resources involves identifying technologies that are not used, not fully used, or simply obsolete for decommissioning and replacement.

  One critical hurdle to optimizing an organization's computing resources is that the organization's staff is reluctant to change. That is, if a computing resource, such as a laptop or desktop computer, is provided to an employee, the computing resource is considered completely personal, that is, the employee actually Regardless of whether or not he or she uses any computing resource, consider that the employee has 100% entitlement to that computing resource. This means that if an employee needs computing resources, the employee has 100% entitlement to these computing resources, and the employee has confidence that the computing resources will be available It is given to the staff. As a result, the employee is free for other computing resources that feel that they are entitled to less than 100% of the computing resource, such as a shared computing resource environment, eg, a virtualized computing environment. I hate to give up these computing resources. This means that even if the employee is not using or at least not fully using computing resources, there is a risk that new computing resources will not be available when needed. Rather than take the risk, we want to maintain a guarantee that the computing resource will be available to you regardless of how inefficient or outdated these computing resources are. Do not give up.

  In addition, known mechanisms allocate computing resources to employees, but determine whether employees are using those computing resources to achieve their business objectives. There is no mechanism to judge. Therefore, often a computing resource is allocated to an employee who uses that computing resource, but that computing resource is used to achieve its intended business purpose. You may not be using your computing resources for other purposes. Thus, although computing resources may appear to be used, they are not actually used to benefit the organization. There are no known mechanisms for detecting such situations and handling them to optimize the benefits of computing resource allocation to the organization.

  Unused, underused, or outdated computing resources for new computing resources in a way that gives confidence that new computing resources will meet employee demands A mechanism is needed to encourage employees to give up. In addition, it detects situations where the computing resource is not being used to achieve the business purpose for which it was allocated, and allocates the computing resource to the benefit of the organization as a whole. A mechanism is needed to handle such situations to optimize.

  Furthermore, support for defining “use” of computing resources beyond the execution of atomic workloads is currently weak. The “use” of computing resources, as the user thinks, is typically done on a time scale in months rather than seconds, encapsulating any number of different interleaved workloads and having a duration of multiple days It includes the expected period of non-use that may become. Thus, optimizing an environment where “use” is defined in this way is much more complicated than simply finding an execution environment for the atomic workload.

  In one exemplary embodiment, a method is provided for reserving computing resources of a data processing system in a data processing system that includes at least one computing device and a plurality of computing resources. The method includes establishing, by at least one computing device, one or more computing environment entitlement agreement (CEEC) data structures, each CEEC data structure having a contracting party and data. Define the terms of a business level agreement with a processing system provider. A CEEC condition specifies a set of computing resources with a specified configuration that will be used by a contracting party for a specified purpose at a specified level and pattern intensity for a specified period of time. .

  The method further includes associating the set of one or more computing resources with the CEEC data structure by at least one computing device. Moreover, the method includes managing a set of one or more computing resources with at least one computing device in accordance with an associated CEEC data structure. The contracting party has failed to use computing resources within the selected computing resource cohort for the specified purpose, for the specified purpose, at approximately the specified level and pattern strength for approximately the specified period. In response, the data processing system logic invalidates the CEEC data structure.

  In other exemplary embodiments, a computer program product comprising a computer usable or readable medium having a computer readable program is provided. The computer-readable program, when executed on a computing device, causes the computing device to perform various of the operations outlined above with respect to the exemplary embodiments of the method and combinations of the operations.

  In yet another exemplary embodiment, a system / apparatus is provided. The system / apparatus can include one or more processors and one or more memories coupled to the one or more processors. When the one or more memories are executed by one or more processors, one or more of various operations and combinations of operations outlined above with respect to exemplary embodiments of the method. Instructions to be executed by other processors.

  These and other features and advantages of the present invention are set forth in the following detailed description of example embodiments of the present invention, or will be apparent to those skilled in the art in view of the detailed description.

  The invention and its preferred uses and additional objects and advantages are best understood by referring to the following detailed description of illustrative embodiments when read in conjunction with the accompanying drawings.

FIG. 3 is an example diagram of a distributed data processing system that can implement aspects of example embodiments. FIG. 6 is an illustration of a diagram of a computing device that can implement aspects of example embodiments. FIG. 2 is an example diagram of a primary operating computing device for implementing a computing environment entitlement agreement based management system in accordance with an illustrative embodiment. FIG. 3 is an example diagram of a computing environment entitlement agreement (CEEC) data structure in accordance with an illustrative embodiment. 5 is a flow diagram outlining an example operation for generating a cohort association according to an exemplary embodiment. FIG. 4 is an example diagram of a cohort profile data structure according to an exemplary embodiment. 5 is a flowchart outlining an example operation for generating a computing resource usage score according to an exemplary embodiment. An example embodiment outlines an example operation for selecting exemplar and counter-exemplar members of a cohort and using this selection to define metrics and related actions in a profile It is a flowchart to do. 6 is a flowchart outlining an example operation for assessing CEEC compliance in accordance with an exemplary embodiment. FIG. 6 is an example diagram illustrating various phases of a transaction for an example CEEC market maker-based scenario in accordance with an illustrative embodiment. FIG. 4 is an example diagram illustrating various phases of a transaction for an example CEEC market maker-based scenario in accordance with an illustrative embodiment. FIG. 4 is an example diagram illustrating various phases of a transaction for an example CEEC market maker-based scenario in accordance with an illustrative embodiment. FIG. 4 is an example diagram illustrating various phases of a transaction for an example CEEC market maker-based scenario in accordance with an illustrative embodiment. FIG. 4 is an example diagram of a transaction specification according to an exemplary embodiment. FIG. 4 is an example diagram of a transaction specification according to an exemplary embodiment. FIG. 4 is an example diagram of a transaction specification according to an exemplary embodiment. 6 is a flowchart outlining an example operation for performing a CEEC market transaction according to an exemplary embodiment.

  The illustrative embodiments provide a mechanism for managing an organization's computing resources in accordance with an established computing environment entitlement agreement (CEEC). Through the mechanisms of the illustrative embodiments, a CEEC is established for each pair of contracting parties that have one or more computing resources of the organization, typically a large organization can have various types, configurations, functions, and uses. Have hundreds or thousands of computing resources. CEEC pairs contracting parties with the appropriate computing resources, evaluates the contracting party's usage with respect to the computing resource with which the contracting party is associated, and dynamically moves the contracting party to the appropriate computing resource. And is used as a basis for matching CEEC buyers and sellers in the market maker environment and performing other computing resource management operations as described below. The As a result of the management of computing resources based on these CEECs, workload balancing, migration, management, etc. can be performed to optimize the usage of computing resources of the organization. However, this workload management, balancing, migration, etc. is the result of management of computing resources based on these CEECs and is not a primary mechanism for achieving efficiency. That is, the target of optimization is the entire managed computing environment, and any given workload execution is a small part of it and does not inherently and naturally define the CEEC.

  A CEEC was configured in a given way for a given business purpose (and thus a computing environment) at a given level and pattern of intensity of utilization for a given period of time. A set of one or more computing resources (eg, hardware and / or software resources) used by a given contracting party, eg, an individual, organization, department, department, company, etc. It is an explicit agreement to do. This given level can be expressed, for example, as a minimum, maximum, average, or other representation of the amount of utilization. Usage intensity patterns can be defined for a given level of usage at one or more specified intervals in a given time period. The CEEC can further define migration criteria that limit the migration functionality for migrating the CEEC from one set of computing resources to another. Each CEEC has one or more accessible by the hardware or software or both mechanisms of the exemplary embodiments to achieve the computing resource management operations specified below. Can be defined in the data structure.

  The data processing system in accordance with the illustrative embodiments provides the computing contract regardless of whether the identified contracting party is actually using the set of computing resources specified in the CEEC for the specified business purpose. A set of resources or a portion of the set of computing resources is reserved for use by an identified contracting party, and the contracting party sets the computing resource set for a specified business purpose within the terms of the CEEC. Includes logic to enforce the CEEC for both the data processing system and the identified contracting party so that the CEEC is invalidated if it is missed. That is, regardless of whether the contracting party actually uses CEEC-based computing resources, CEEC can be enforced on the provider side of the computing resources. However, at the same time, if a contracting party fails to use a computing resource that is subject to CEEC in accordance with the terms of the CEEC, the CEEC will not be able to enforce either the computing resource provider or the contracting party. Can be overridden or denied by the processing system. Typically, such denials are combined with replacing this CEEC with a new CEEC that is more suitable for the use of computing resources by the contracting party, and the new CEEC is probably related to the CEEC that was denied at this time. Associated with a new set of one or more computing resources that may be different from the previous set of one or more computing resources. The authority over denial of CEEC is granted by the company / organization that ultimately owns and benefits from the use of computing resources and employs one or more mechanisms of exemplary embodiments of the invention. Provided.

  CEEC also provides a mechanism whereby it can perform entitlement pricing and trading on computing resources within the market maker mechanism. This market maker mechanism establishes a market for exchanging CEEC and, consequently, its associated workloads and functions, but it is not a “free market”. This is a market owned by a particular organization and is managed to be a particular interest of a particular organization. That is, rather than simply determining the highest bidder as in a free market system, the CEEC market maker in the illustrative embodiments allows CEEC buyers and sellers within a market where a particular organization is established. Allows you to define preferences for matching. Thus, market factors are utilized by specific companies / organizations to ensure that their value is enhanced and recognized by transactions executed through the market.

  A simple example of CEEC and its use within a CEEC-based computing resource management system is that companies such as International Business Machines (IBM), Armonk, NY, provide employees with laptop computers. It is. In this scenario, the computing resource can be considered a laptop computer, the contracting party can be considered an employee, and the provider of computing resources is an enterprise, i.e. IBM. When allocating a laptop to an employee, the provider's requirements for providing the laptop to the employee, that is, the employee's entitlement to computing resources, and a given period (eg, 1 year) , At a given level and pattern usage intensity (eg, laptop resource usage at about 50% usage during working hours from 9am to 5pm Monday to Friday) Configured in a way (eg having a specific set of hardware and / or software to achieve a specified business purpose) and using a laptop for a specified business purpose (eg software development) A CEEC is established to manage both employee requirements. The system then monitors the employee's use of the laptop according to the CEEC and, based on the conditions specified by the CEEC, transfers the CEEC between the buyer and seller, denies the CEEC, The creation of a new CEEC for the user can be controlled.

  For example, the data processing systems of the illustrative embodiments can determine how often an employee uses a laptop, what process is performed when the employee is using a laptop, Any other that indicates how much of the laptop's resources will be used when the employee is using the laptop, as well as employee usage with respect to CEEC-based computing resources (laptops) By determining the metrics, the employee's use of the laptop can be monitored. This monitor can determine whether an employee is using a laptop within a given tolerance of the conditions specified in the CEEC, otherwise the actual use of the laptop by the employee It can be determined whether the difference between the situation and the expected usage based on the CEEC justifies the exchange of the CEEC, the negation of the CEEC, and the creation of a new CEEC, or other management action. Whether the computing resource (s) associated with CEEC is exchanged by a computing resource provider, eg, IBM, as a result of an exchange, denial, creation, or other management action associated with CEEC Or other methods of collection and / or redistribution may occur.

  This example is used for two laptops, one for traditional business use during basic business hours, and an occasional customer demo about once a month, It can be extended to take into account another one that is not expected to be used between demos. These are two different CEECs that encapsulate very different expectations about the strength of use of this pattern.

  It is further enhanced to add a server that hosts a customer-facing website that is expected to be used at a medium level of strength, ie 24/7, and is available to customers for all time zones. can do. Again, a completely different CEEC encapsulates very different expectations about the intensity of use of this pattern.

  Accordingly, the CEEC is a computing resource for providing a particular set of computing resources or a particular computing environment that is to be used by a contracting party in a particular manner. In addition to specifying the requirements regarding the provider, the requirements regarding the contracting party regarding the usage status of the contracting party are also specified. This is in some ways significantly different from the known contractual relationships between contracting parties, for example, between users and providers of computing resources. For example, one known contractual relationship between a user and provider of computing resources is known as a quality of service guarantee (SLA). With SLA, a provider of computing resources can use a general point, for example, a specific bandwidth, bit rate, etc., without specifying a specific computing resource in exchange for payment by a user for a specified level of service. It is required to provide a certain level of service defined in terms of mean failure time, mean repair time, jitter, etc. There is no requirement for users to use computing resources, such as for any particular business purpose, at a specified level of strength or usage pattern, and the only requirement for users is that Pay for a specified level of service.

  Similarly, another type of contractual relationship between users and providers of computing resources is, for example, the required bit rate, delay, jitter, packet drop probability, bit error rate, etc. Known as a Quality of Service (QoS) agreement, where a certain level of performance guarantee of the data flow is provided. Again, there is no requirement on the user side to use the system, such as for a specific business purpose, at a specified level of strength or usage pattern. The only requirement for the user is that the user pays for this quality of service.

  Traditional cloud computing mechanisms are based on such SLA or QoS type conventions. That is, if a user subscribes to a cloud computing resource provider, the user has a contract for a general level of service or quality of service, and how this level of service or quality of service is provided. Is not worried about, i.e., what specific computing resources are used to provide this level of service or quality of service. Users simply access the “cloud” and this particular level of service or quality of service is whatever the way the cloud resource provider has decided to provide this level of service or quality of service. Expect to be offered. Again, there is no requirement on the user side to actually use the resources of the cloud, such as for any specific business purpose, at any minimum level of strength, at any minimum usage pattern. The user only needs to pay for the SLA or QoS requested from the cloud. To explain this in other ways, the cloud is non-judgmental when considering resource usage, that is, if there is a business goal for which a set of allocated resources is used, that business goal I don't know or worry about what is. This is in contrast to the company's goal in allocating capital to the cloud, that is, the company does this in the hope of achieving a particular performance.

  Thus, one of the main differences between CEEC and the known contractual relationship between a contracting party and a computing resource provider is that the contracting party within the CEEC has the computing resource (s) at the prescribed level. Otherwise, the CEEC may be negated or exchanged, etc., and the contracting party will be responsible for the relevant computing and It may be necessary to waive the contracting party's entitlement to the resource (s). A new CEEC may be generated if the existing CEEC is denied, or the existing CEEC may be reassociated with a new set of one or more computing resources. A new or new association of an existing CEEC will most closely match which computing resource best fits the usage of the computing resource by the contracting party or a requirement based on the new or existing CEEC. To determine whether they are in agreement or both, it can be performed based on the measured usage of the computing resource (s) by the contracting party as well as the capabilities, configuration, etc. of the computing resource. All of this is performed within an organization's market established by a market maker system operating on a set of CEEC and computing resources, as described below. This organization's market is defined in terms of achieving the most efficient allocation of computing resources to contracting parties, as defined by criteria specified by the organization. These and other mechanisms of exemplary embodiments are described in more detail below.

  As will be appreciated by one skilled in the art, the present invention may be implemented as a system, method, or computer program product. Accordingly, aspects of the invention may be in the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects. Either of which can be generally referred to herein as a “circuit”, “module”, or “system”. Furthermore, aspects of the invention may take the form of a computer program product implemented on any one or more computer readable media having computer usable program code implemented thereon.

  Any combination of one or more computer readable media may be used. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (non-exhaustive list) of computer readable storage media include electrical connections with one or more wires, portable computer diskettes, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any of the above Appropriate combinations will be included. In the context of this specification, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device.

  A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal can take any of a variety of forms including, but not limited to, electromagnetic, light, or any suitable combination thereof. The computer readable signal medium is not a computer readable storage medium but any computer readable storage medium capable of communicating, propagating, or transporting a program for use by or in connection with an instruction execution system, apparatus, or device. can do.

  Program code embodied on a computer readable medium may be any suitable medium including, but not limited to, wireless, wired, fiber optic cable, radio frequency (RF), etc., or any suitable combination thereof. Can be used and transmitted.

  Computer program code for performing the operations of aspects of the present invention is Java (Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle Corporation or its affiliates or both), Can be created in any combination of one or more programming languages, including conventional procedural programming languages such as Smalltalk ™, object oriented programming languages such as C ++, or “C” programming language or similar programming languages it can. The program code is entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on the remote computer, or completely on the remote computer Or it can run on the server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN) (eg, Internet In some cases, a connection is made to an external computer (via the Internet using a service provider).

  Aspects of the invention are described below with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to example embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions are provided to a general purpose computer, special purpose computer, or other programmable data processing device processor, and the instructions executed by the computer or other programmable data processing device processor are flowcharts or block diagrams. Or a machine can be produced that creates a means for implementing the function / action specified in one or more blocks of both.

  These computer program instructions also include an apparatus (article of instructions) that includes instructions that cause instructions stored on a computer readable medium to implement a function / action specified in one or more blocks of a flow diagram and / or block diagram. It can also be stored on a computer readable medium that can be directed to a computer, other programmable data processing apparatus, or other device to function in a particular way.

  Also, computer program instructions are loaded onto a computer, other programmable data processing apparatus, or other device, and instructions executed on the computer or other programmable apparatus are either flowcharts and / or block diagrams. Or on a computer, other programmable data processing apparatus, or other device to produce a computer-implemented process that provides a process for implementing a function / action specified in multiple blocks A series of operation steps can also be executed.

  The flowcharts and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations for systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram represents a module, segment, or portion of code that includes one or more executable instructions for implementing the specified logical function (s). be able to. It should also be noted that in some alternative implementations, the functions shown in the blocks may be performed out of the order shown in the drawings. For example, two blocks shown in succession may actually be executed at about the same time, or sometimes in reverse order, depending on the function involved. Also, each block in the block diagram and / or flowchart diagram and combinations of blocks in the block diagram and / or flowchart diagram can be used in a special purpose hardware-based system or special purpose hardware and computer that performs a specified function or act. It will also be noted that this can be realized in combination with instructions.

  Accordingly, the exemplary embodiments can be used in a wide variety of types of data processing environments, including distributed data processing environments, single data processing devices, and the like. To provide a context for the description of particular elements and functions of exemplary embodiments, FIGS. 1 and 2 are described below as example environments in which aspects of the exemplary embodiments may be implemented. Provided to. It should be appreciated that FIGS. 1-2 are only examples and are not intended to assert or imply limitations regarding the environment in which aspects or embodiments of the present invention may be implemented. Many changes may be made to the depicted environment without departing from the spirit and scope of the invention.

  Referring now to the drawings, FIG. 1 depicts a diagrammatic representation of an example distributed data processing system that can implement aspects of exemplary embodiments. The distributed data processing system 100 can include a network of computers that can implement aspects of the exemplary embodiments. Distributed data processing system 100 includes at least one network 102 that is a medium used to provide communication links between various devices and computers connected together in distributed data processing system 100. Network 102 may include connections such as wires, wireless communication links, or fiber optic cables.

  In the depicted example, server 104 and server 106 are connected to network 102 along with storage unit 108. In addition, clients 110, 112, and 114 are also connected to network 102. These clients 110, 112, and 114 can be, for example, personal computers, network computers, and the like. In the depicted example, server 104 provides data such as boot files, operating system images, and applications to clients 110, 112, and 114. In the depicted example, clients 110, 112, and 114 are clients to server 104. Distributed data processing system 100 may include additional servers, clients, and other devices not shown.

  In the depicted example, the distributed data processing system 100 includes a network 102 that represents a global collection of multiple networks and gateways that communicate with each other using a transmission control protocol / Internet protocol (TCP / IP) protocol suite. Including the Internet. At the heart of the Internet is the backbone of high-speed data communication lines between large nodes or host computers, consisting of thousands of commercial, governmental, educational, and other computer systems that route data and messages. Of course, the distributed data processing system 100 may also be implemented to include several different types of networks, such as, for example, an intranet, a local area network (LAN), a wide area network (WAN), and the like. As noted above, FIG. 1 is intended as an example, and not as an architectural limitation for different embodiments of the present invention, and thus the specific elements shown in FIG. It should not be regarded as a limitation on the environment in which the exemplary embodiments can be implemented.

  Referring now to FIG. 2, a block diagram of an example distributed data processing system is shown in which aspects of example embodiments may be implemented. Data processing system 200 is an example of a computer, such as client 110 in FIG. 1, in which computer usable code or instructions implementing the processes for the illustrative embodiments of the present invention may be located.

  In the depicted example, data processing system 200 includes a North Bridge and Memory Controller Hub (NB / MCH) 202 and a South Bridge and Input / Output (I / O) Controller Hub (SB / ICH) 204. Use a hub architecture. Processing unit 206, main memory 208, and graphics processor 210 are connected to NB / MCH 202. Graphics processor 210 may be connected to NB / MCH 202 via an accelerated graphics port (AGP).

  In the depicted example, a local area network (LAN) adapter 212 connects to the SB / ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive 230, universal serial bus (USB) port and other communications Port 232 and PCI / PCIe device 234 connect to SB / ICH 204 via bus 238 and bus 240. PCI / PCIe devices can include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, and PCIe is not used. ROM 224 can be, for example, a flash basic input / output system (BIOS).

  The HDD 226 and the CD-ROM drive 230 are connected to the SB / ICH 204 via the bus 240. The HDD 226 and CD-ROM drive 230 may use, for example, an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. A super input / output (SIO) device 236 can be connected to the SB / ICH 204.

  The operating system runs on the processing device 206. The operating system coordinates and controls various components within the data processing system 200 of FIG. As a client, the operating system can be a commercial operating system such as Microsoft Windows 7 (Microsoft and Windows are trademarks of Microsoft Corporation in the United States, other countries, or both). An object-oriented programming system, such as a Java programming system, can be run in conjunction with the operating system and makes calls to the operating system from Java programs or applications running on the data processing system 200 (Java is an Oracle And / or its affiliates).

  As a server, the data processing system 200 can be, for example, an IBM® eServer® System p® computer system running an Advanced Interactive Executive (AIX®) operating system or a LINUX operating system. (IBM, eServer, System p, and AIX are trademarks of International Business Machines in the United States, other countries, or both, and LINUX is a trademark of Linus in the United States, other countries, or both. Registered trademark of Tolballs). Data processing system 200 may be a symmetric multiprocessor (SMP) system that includes a plurality of processors within processing unit 206. Alternatively, a single processor system can be used.

  Operating system, object-oriented programming system, and instructions for applications or programs are located on a storage device such as HDD 226 and can be loaded into main memory 208 for execution by processing unit 206. The processes relating to exemplary embodiments of the present invention are computer usable, for example, which may be located in a memory such as main memory 208, ROM 224, or in one or more peripheral devices 226 and 230, for example. It can be executed by processing unit 206 using program code.

  As shown in FIG. 2, a bus system such as bus 238 or bus 240 may consist of one or more buses. Of course, the bus system is implemented using any type of communication fabric or architecture that allows the transfer of data between different components or devices connected to any type of communication fabric or architecture. be able to. A communication unit, such as modem 222 or network adapter 212 of FIG. 2, may include one or more devices used to send and receive data. The memory can be, for example, a cache as found in main memory 208, ROM 224, or NB / MCH 202 of FIG.

  Those skilled in the art will recognize that the hardware of FIGS. 1-2 may vary depending on implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives can be used in addition to or instead of the hardware depicted in FIGS. . Also, the processes of the illustrative embodiments can be applied to multiprocessor data processing systems other than the SMP systems described above without departing from the spirit and scope of the present invention.

  Moreover, the data processing system 200 includes a number of client computing devices, server computing devices, tablet computers, laptop computers, telephones or other communication devices, personal digital assistants (PDAs), etc. It can take any form of different data processing systems. In some illustrative examples, the data processing system 200 is a portable configured with flash memory to provide non-volatile memory, for example, to store operating system files and / or user-generated data. It can be a computing device. In essence, the data processing system 200 can be any known or later developed data processing system without architectural limitations.

  Many network-based systems, such as those shown in FIG. 1 or equivalent, generate networks of computing resources for organizations, eg, businesses, governments, private organizations, or other legal entities. Can be combined together. These computing resources may include a small set of computing resources, eg, tens to hundreds of computing resources, or a large set of computing resources, eg, various types, functions It may contain thousands of computing resources such as configurations. For example, computing resources can be from desktop computers, laptop computers, printers, routers, switches, storage devices, specific processors, memories or portions of memory, network devices or electrical products within computing devices. Or any other computing resource. Each of these computing resources can be of different types, functions, configurations, etc., for example, the organization can have thousands of desktop computers, some of which are x86 processor Others have cell broadband engine processor chips, others have P7 processor chips, each with a different number of processor cores, different software Resources can be loaded, providing different types, configurations, and functions.

  One or more servers or other computing devices of the organization's computing resource network may be a CEEC-based computing resource management system in accordance with the illustrative embodiments described herein. Can be configured to provide hardware and / or software mechanisms for implementing For example, one or more servers or network connections to establish, store, manage, and associate these CEECs with a particular contract party as well as one or more specific cohorts of computing resources. A storage device or both can be provided. In some exemplary embodiments, the mechanisms of the exemplary embodiments may include existing organizational systems, such as human resource systems, to associate a CEEC with a specific contracting party and a specific computing resource. Can be interfaced with.

  In addition, as a configuration management database (CMDB) system for obtaining and storing configuration information relating to computing resources of the organization's computing resource network, one or more servers or network-connected storage Devices or both can be provided. In addition, one or more servers or other computing devices in the organization's network of computing resources may be used by market maker services, cohort management services, usage, as described in more detail below. It can be configured to provide situation scoring services, transaction builder services, cohort and profile management services, system usage management services, and the like.

Overview of CEEC-Based Resource Management System FIG. 3 is an example diagram of a primary operating computing device for implementing a computing environment entitlement contract-based management system in accordance with an illustrative embodiment. The elements illustrated in FIG. 3 may be implemented as software instructions executed by one or more processors of one or more computing devices such as servers, for example. In some exemplary embodiments, some of the mechanisms shown in FIG. 3 may be implemented as special purpose hardware devices, such as, for example, application specific integrated circuits (ASICs), or, for example, As a combination of hardware such as firmware and software, it can be implemented entirely within the hardware of the computing system. The scope of the exemplary embodiments is, without limitation, purely hardware embodiments as well as any particular configuration that is determined to be appropriate for a particular implementation of the exemplary embodiments. It includes embodiments that include a combination of hardware and software.

  As shown in FIG. 3, a computing environment entitlement agreement (CEEC) based resource management system 300 includes a transaction builder 312, a cohort provided as part of one or more web application servers 310. And a profile manager 314, a system usage manager 316, and a CEEC manager 318. The second web application server (WAS) 320 comprises web services 322-328 including a market maker service 322, a cohort management service 324, a usage scoring service 326, and a CEEC-based reservation service 328. can do. A database system 330 is provided that includes a configuration management database 332 and a CEEC database 334. In addition, the database system 330 can include an interface 336 for interfacing with other organizational or enterprise systems 340, such as one or more human resource systems.

  The system 300 further includes an information technology management system 350 that includes one or more information technology management warehouse systems 352-356. Information technology management warehouse systems 352-356 collect usage information from computing resources associated with the various site systems 360-364 of the organization. This computing resource usage information collected by the information technology management warehouse systems 352-356 is used to evaluate resource usage (such as by usage scoring service 326), CEEC-based computing resource reservation. Perform (e.g., CEEC-based reservation service 328), perform CEEC market-based exchanges (e.g., market maker service 322), perform cohort management on computing resource cohorts and CEEC cohorts (cohort management) All of these can be used by web application services 322-328, all of which are described in more detail below.

  The database system 330 stores various computing resources of the site systems 360-364 and basic configuration information of the CEEC in the CMDB 332 and the CEEC database 334, which guarantees compliance with the CEEC, Resource usage obtained from information technology management warehouses 352-356 to perform various operations by Web application service 320 to execute transactions, perform cohort management, and score resource usage. Used with information. The CMDB 332 includes, for example, daily resource usage metric information, weekly resource usage metric information regarding various computing resources, official resource usage scores obtained from the usage scoring service 326, profile specification, cohort specification, Various tables or various types of other data structures can be stored, including cohort assignments, system configuration information, and system specifications. Information stored in the CMDB 332 includes a cohort and profile manager 314, an information technology monitoring warehouse 352-356, a usage scoring service 326, a cohort management service 324, and a Tivoli configuration manager, a Tivoli application dependency discovery It can be obtained from configuration scanning and discovery clients such as manager, HP open view, MS system center configuration management client. A profile specification is an attribute of a profile that is used to interpret usage data, such as whether to use best or moving average data, a weekly or daily time scale, and how many days It is an attribute such as whether history should be considered.

  Information stored in the CEEC database 334 may be obtained from CEEC conditions and criteria user specifications specified via the CEEC manager interface 318. This information can also be augmented with information obtained from a cohort management service 324 that identifies a cohort of CEECs associated with the CEEC listed in the CEEC database 334. Moreover, the CEEC can be associated with a particular ID of a particular computing resource or cohort of computing resources via the CEEC database 334. The contents of the CEEC will be described in more detail below.

  Web application service 310 is a market-based exchange of CEEC by market maker service 322, cohort and profile management by cohort and profile manager 314, system usage management by system usage manager 316, and by CEEC manager 318. For use in performing CEEC management, a web application service is provided that is user accessible to perform various operations including constructing a transaction by transaction builder 312. The use of these various user accessible web application services 310 is described in more detail below.

Computing Environment Entitlement Agreement (CEEC)
The basic element upon which the illustrative embodiments are built is the Computing Environment Entitlement Agreement (CEEC). A CEEC is a computing resource (eg, defining a computing environment) configured in a specific way for a specified business purpose at a given level and pattern usage intensity for a given period of time (eg, , Hardware (CPU, RAM, disk IO, network IO, disk storage, etc.), software, physical infrastructure (heating, cooling, rack space, etc.) or services (management, IT security, backup, etc.), or these An explicit agreement that a particular set of) is used by a given contracting party, eg, an individual, organization, department, department, company, or other legal entity. The CEEC can be specified in terms of one or more data structures that specify various conditions of the CEEC, including the CEEC characteristics outlined above. That is, the one or more data structures specify a particular set of computing resources with which the CEEC is associated, either individually or with respect to the cohort of computing resources described below. One or more data structures may be defined for a given computing resource hardware / software specific configuration in which CEEC is valid, a specific business purpose of the CEEC, a given quantifiable metric over a given period of time. It further specifies the strength of use of the level and pattern, and a given period during which the CEEC is valid unless negated by not meeting the requirements of the CEEC as described below. In addition, the one or more data structures can specify conditions for CEEC transition and / or denial.

  FIG. 4 is an example diagram of an example CEEC data structure, according to an illustrative embodiment. As shown in FIG. 4, the CEEC data structure 400 includes a CEEC ID field 410 for designating identification information of the CEEC to which the CEEC data structure 400 corresponds. The CEEC ID in field 410 associates the CEEC with another CEEC cohort, a cohort of computing resources, and performs other management actions including CEEC migration, CEEC negation and replacement, etc. by the market maker mechanism It is a unique ID that identifies the CEEC for use in doing so. In essence, the CEEC ID is used as a method to clearly distinguish this CEEC from other CEECs managed by the CEEC-based resource management systems of the exemplary embodiments.

  The CEEC data structure 400 further includes a contract party ID field 420 that stores a contract party ID to clearly identify the contract party or group of contract parties with which the CEEC is associated. For example, the contract party ID in field 420 can be a unique employee ID, department or work group ID, user ID, and the like. This contract party ID can be used to correlate information from other enterprise systems, such as human resource systems, so that information from these other systems is identified by the CEEC data structure 400. Can be associated with the CEEC. This contract party ID may also be used to correlate usage metric information and / or scoring metrics obtained from such usage metric information, for example, with a particular contract party and corresponding CEEC. You can also.

  The CEEC data structure 400 includes the computing resource (s) with which the CEEC is associated, the computing resource cohort, or other group of computing resource (s), and the required hardware associated with them. It further includes one or more computing resource ID fields 430 for clearly identifying the hardware / software configuration. That is, for example, the computing resource (s) of an organization's data network (s) can be organized into a cohort as described below, and the CEEC is a cohort of computing resources. Can be associated with Alternatively, a CEEC can be associated with a specific computing resource, such as a laptop, desktop computer, etc. Specific configuration information specifies, for example, the number and type of processors, the amount of available memory, the amount of available bandwidth, the specific software package installed, or the business purpose for which the CEEC was established Any other identifiable component that is required to be specified can be specified.

  In addition, the CEEC data structure 400 may include one or more to identify one or more specified business purposes that may use the computing resource (s) identified in the field 430 according to the CEEC. The field 440 is further included. That is, a project, work group, department, or other organizational division of an organization relates to the business purpose ID to which the organizational division is applied, eg, software development, specific project, accounting, management, human resources, etc. It can be associated with a unique ID. Specific computing resources can also be associated with these business purpose IDs, for example, specific software packages can be associated with specific business purpose IDs, for example, XYZ accounting software supports “accounting” XYZ word processing software can be associated with a business purpose ID such as “Management”. A particular computing resource, a cohort of computing resources, etc. can be associated with more than one business purpose ID. Moreover, generic business purpose IDs can further be provided for computing resources that are typically used for all business areas, such as word processing software, specific server computing devices, and the like. Field 440 may include a business purpose ID with which the CEEC is associated so that the CEEC identifies for which business purpose the computing resources identified in field 430 can be used.

  The CEEC data structure 400 includes one or more fields 450 for specifying computing resource configuration requirements to satisfy CEEC conditions and a contract to use the computing resources specified in field 430. It further includes one or more fields 460 for specifying the usage level of the given level and pattern for the given time period required by the parties. The level and pattern of usage intensity can be specified in a wide variety of ways, but is generally a field 430 that is required for a specified period of time for a contracting party to use computing resources in accordance with the conditions and requirements of the CEEC. This is for specifying the usage amount of the computing resource specified in. For example, field 460 indicates that the contracting party must use computing resources for the specified business purpose for at least 50% of the time between 9:00 am and 5:00 pm on weekdays in order to comply with the terms of the CEEC. Can be specified. Usage patterns can be defined in terms of an exemplary system that can describe daily, weekly, and monthly peak / trough heights and durations, along with tolerances such as percentage tolerance. Such patterns can be recorded numerically either directly or, for example, via a visualization interface.

  The CEEC data structure 400 may optionally further include a field 470 for specifying migration criteria for migrating the CEEC from one computing resource, a cohort of computing resources, and the like. For example, the field 470 can migrate the CEEC data structure 400, ie, the types of computing resources that can be re-associated, the minimum requirements or configuration of these computing resources, the CEEC data structure 400. Specific conditions can be specified. For example, a desktop workstation using 3 gigabytes of RAM and 500 gigabytes of local storage with an x86 CPU, running a Windows server 2008, and acting as a departmental storage server runs on an x86 or x86-64 CPU, Only virtualized clusters with more than 3 Giga usable memory could be migrated. In addition, the CEEC could specify that the wattage (weighted average) consumed at the destination is 50 watts or less, as opposed to, for example, a desktop wattage of 400 watts.

  The CEEC described by the CEEC data structure 400 is not an inseparable entity, but instead allows various computer resource providers to work together to provide the computer resources necessary to meet CEEC requirements. Can be segmented. That is, a CEEC does not have to be established between two parties, i.e., between a single computing resource provider and a single contracting party, but may be established between three or more parties. it can. Thus, for example, one computing resource provider can satisfy a first subset of CEEC conditions (eg, processor throughput) and a second computing resource provider can satisfy CEEC conditions. If a second subset of (eg, storage capacity) can be satisfied, the CEEC is 1 for each condition of the original CEEC that must be satisfied separately by the individual computing resource provider. Each can be partitioned into individual sub-CEECs. This eliminates the requirement that a single “buyer” must satisfy all of the requirements of a CEEC that is “sold” via the CEEC market, as described in more detail below. Increases the chance of a successful transaction. Since known workload management mechanisms typically move workloads as a whole that are inseparable, this further differentiates the CEEC mechanisms of the exemplary embodiments from such known workload management mechanisms. It is.

  The CEEC data structure 400 of the exemplary embodiments is created by a system administrator or other privileged user when a privileged user wishes to allocate one or more computing resources to a particular contracting party. be able to. In other cases, the CEEC data structure 400 is CEEC-based, such as when negating an existing CEEC and responding to a determination that there is a need to replace the denied CEEC with a replacement CEEC, as described in more detail below. Can be created automatically by the resource management system. Assuming that a privileged user is creating the CEEC data structure 400, this CEEC data structure 400 can be created, for example, via a user interface that can be provided by the CEEC manager 318 of FIG. . This user interface provides computing resources, a cohort of computing resources for privileged users who can log in to the CEEC manager using security login procedures commonly known in the art. As well as various lists of contract party information, options for specifying various other parameters in fields 410-470 of CEEC data structure 400 may be presented. Information for entry into such a list can be obtained, for example, from CMDB 332, cohort management service 324, usage scoring service 326, or other elements of system 300 of FIG. 3, or combinations thereof. . The resulting CEEC data structure 400 is stored in the CEEC database 334 of FIG. 3 for later access by other elements of the system 300 to perform CEEC-based resource management operations, as described below. be able to.

  Thus, CEEC forms the basis for performing resource management in the systems of the exemplary embodiments. CEEC is not only responsible for computing resource providers to provide specific computing resources configured in a specific way, but also for specific business purposes at specified levels and intensity of pattern usage. It differs from other computing resource arrangements in that it also defines the contracting party's obligations to use computing resources. Known computing resource arrangements impose obligations on providers to provide general computing resource capabilities, such as bandwidth, storage capacity, etc., but specific computing resources and It does not identify that particular configuration and, more importantly, does not specify usage requirements on the part of the contract. On the other hand, the sole obligation of the contracting party depends on the level, pattern or strength of use of these computing resources, regardless of whether the contracting party actually uses these computing resources. And pay for computing resources regardless of the business purpose of their use.

  Using CEEC as a basis, system 300 includes logic to operate on CEEC to perform resource and contract party entitlement management. This logic needs to determine compliance with the conditions, determine the need to negate and replace the CEEC, move the CEEC from one cohort with CEEC and / or one with computing resources to another Providing a market maker mechanism to facilitate gender determination, CEEC denial, replacement, and transition, and provide CEEC terms to both computing resource providers and contracting parties. Provide a mechanism for enforcement. In addition, this enforcement handles the workload on the appropriate (existing or planned) computing resource according to the association between the CEEC and the computing resource, such as by the cohort mechanism described below. Includes routing.

  As an example of the CEEC and CEEC mechanism of exemplary embodiments, assume that a particular organization has a business objective of “DB2 performance testing”. In addition, to run this DB2 performance test, it runs RHEL server 5.5, consumes 1500 watts, features Gigabit Ethernet and fiber attached SAN storage, and has 24/7 on-call management support. Suppose a computing environment with 16 cores, 64G RAM, 10 terabyte system is needed. These conditions are all conditions that would be identified in the CEEC conditions. Any number of DB2 performance test activities can be performed on a computing system that meets these requirements based on the terms of this CEEC, and this CEEC essentially accomplishes the purpose of the DB2 performance test activities. In order to perform activities on this computing system, a user's entitlement to computing system resources is defined.

  If the DB2 performance test workload is scheduled to be migrated to a different computing system, the CEEC must first be migrated, resulting in the user having confidence in the destination computing system. The new computing system will ensure that not only new or different assets (computing systems) will be provided, but all the necessary elements of the computing environment will be provided as required under CEEC It is also composed of CEEC. In addition, the user is obligated to execute the workload associated with the DB2 performance test in order to perform his side of the CEEC, i.e. the user is responsible for business use, for example the output of the DB2 performance test. In exchange, a computing environment is provided.

  The exemplary embodiments would not be sufficiently confident to make a transition without it, without which the input workload strength and size defined for the extended time scale would be completely Facilitate the migration of the workload (s) by joining the contracting party to the new CEEC that may not be notified. Further, as described in more detail below, the CEEC market maker mechanism of the exemplary embodiments allows an organization or company to correctly price on both sides of the transaction, although this is what is being considered. Neither is possible if the calculation workload is arranged. That is, the price can be assessed to the cost of the existing environment, the price can be assessed to the cost of the destination environment, and the price can be assessed to the value of the business output. These prices can be compared with any or all alternatives to determine which transition is most beneficial.

CEEC Market Maker Mechanism Overview One of the main objectives of CEEC is to enable pricing and trading of entitlements to computing resources to optimize the usage of computing resources in an organization or enterprise. In this case, optimization is defined as achieving one or more desired business goals for the usage of computing resources owned by the organization or company. This price assessment allows for the establishment of a CEEC market for entitlement price assessment and trading, but this is not a free market. In contrast, the CEEC market is a market owned by the organization or company, designed and functioning according to parameters and rules defined and designed to benefit the organization / company. In a free market, anything that indicates that the party to the transaction is a good transaction (eg, the highest price in exchange for a particular product) is a good transaction. However, in the CEEC market, the organization or company does not benefit most from the parties to the transaction, ie, the buyer and seller of the transaction, but which transaction benefits the organization or company's goals. Define what is a good transaction based on what Market factors are therefore used by the organization and companies to ensure that their value is enhanced and realized by trading momentum.

  Exemplary embodiments allow you to price transactions in relation to the organization or company and increase the number of beneficial transactions in relation to the organization or company that represents its goal by market factors Become. As a result, the CEEC and its associated entitlements and obligations are negated, replaced, and transitioned between the CEEC cohort, the computing resource cohort, the contracting parties, etc., all of which are final for the organization or company. This situation will be kept to a minimum, although it may be done in a way that is beneficial and sometimes harmful to individual contracting parties.

  For example, an enterprise may have an urgent need to migrate from a specific platform, yield a specific physical space, or decommission a set of dedicated resources. These are three examples where a company can deny CEEC simply because its profits require such denial. Denied CEEC holders will be forced to enter the market to find buyers. Such buyers have their CEEC denied due to inadequate use and will have available capacity on the desired platform (for the company).

  Workload migration is one of the results of this CEEC market process. However, workload transfer is not the ultimate goal of CEEC. Its purpose is to efficiently allocate entitlements for computing resources and resource usage via market factors. This involves encapsulating all of the obligations, conditions, and considerations that are part of the execution of a given computing environment (ie, computing resources and their configuration) into a single contract, ie, CEEC. Rather, the CEEC defines how a contracting party can use and must use computing resource (s) allocated to a known business purpose, such as by terms of use. “Workload” is executed within the parameters of the CEEC and, if the CEEC is migrated, only as a result of trading one CEEC with another CEEC according to any migration criteria that can be specified for the migrated CEEC Can be done that way.

  CEEC has value in static situations to ensure the proper use of assets, but there are thousands of potential pairs and many organizations that restrict the free flow of workloads or When an enterprise attempts to move a user (ie, a contracting party) from one or more computing resources to another computing resource (eg, from one hardware environment to another) In particular, the need for CEEC is particularly acute. That is, the workload does not flow naturally to more efficient means. For example, as previously described above, a user may share sufficient functionality possessed by an existing computing resource, regardless of how well the existing computing resource is being used. Because you cannot be certain that the resource will provide, in exchange for shared access to more efficient computing resources, your existing potentially inefficient computing resources that are not fully used (for example, , Often hate to give up the laptop).

  More simply, employees have newer and more efficient computing resources (eg, on a server) even if they are not using a 100% eligible laptop. Newer and more efficient computing resources if you take your current laptop, even if you don't use it, because you don't have a subjective sense of guarantee that it will be accessible to you You may be reluctant to give up that laptop in exchange for shared access to. That is, people tend to protect the old trap of “one of the hands is worth two in the bush”, and the best interests of the organization or the entire enterprise in terms of computing resources and their efficient allocation They tend to be more worried about their immediate needs, for example, “I know that my project needs an amount of storage space of X. I do n’t know or care if I need it. ”

  Complex organizations address the geographical scope, departmental budgeting, and capital-to-spending disparities that make it difficult to buy more efficient hardware instead of using current inefficient hardware. There are also many non-technical barriers to the computational workload flow that must be made. Optimization is generally achieved through centralization and consolidation, but organizational boundaries often impede such activities. CEEC is a mechanism that allows this fault to be cleared.

  Due to the mechanisms of the exemplary embodiments, a CEEC market maker such as the CEEC market maker service 322 of FIG. 3 must formally enumerate along with basic technical requirements (CPU, RAM, etc.) by the contracting party. Enables you to trade an existing CEEC with a new CEEC that can contain all of the elements (network bandwidth, storage space, management support, etc.) that must not be. Transactions even when settlement issues occur (you can be satisfied by holding an organization or company's idle inventory and sometimes purchasing slightly more than what is explicitly requested) As long as the CEEC market maker can provide the contracting parties with confidence that their usage requirements will be met, such as by having the necessary logic and mechanisms to complete both sides of the The important thing that suppresses strict allocation is eliminated. In addition, CEEC allows groups within an organization to provide resources to the shared pool while still retaining all rights to its existing resource requirements by CEEC.

  Again, it should be noted that the CEEC market maker mechanism of the exemplary embodiments does not function as a neutral third party. On the contrary, this mechanism acts as a driving force to drive an organization or company in a particular direction, and thus works by creating and manipulating market factors biased in that particular direction. A CEEC market maker, for example, a CEEC market maker service 322, adds to the CEEC market maker's hardware / software logic to optimize the efficiency (workload) of all usage in the market (organization or enterprise). Has the specific intent expressed. Specific ways for the CEEC market maker mechanism to drive the market to achieve the operational goals of an organization or company include selection of buyers, sellers, and buyer-seller pairs in the CEEC market, and It can be defined in a set of CEEC market parameters, rules, etc. that are processed by the CEEC Market Maker mechanism to control the creation, denial, and replacement of the CEEC by the CEEC Market, collectively referred to as a “profile”. Moreover, this can be performed on a defined collection of computing resources and / or CEEC, referred to herein as a “cohort”, to define such a cohort and its membership Is specified in the CMDB.

  Within the CEEC market, sellers and buyers do not themselves decide to become sellers and buyers. In contrast, the CEEC market maker mechanism represents organizational or corporate goals, identifies computing resources and CEEC cohorts, and determines which elements should be buyers and sellers Apply the profile to usage and configuration data. The profile applied is how to combine multiple computing resources into a cohort and the basis for that (the term “cohort” means “collection” of similar elements), combining multiple CEECs And how to become a cohort and the basis for that, how and when usage information indicates the need to change cohort membership, and when computing resources or cohorts should be considered as buyers or sellers of CEEC A set of one or more parameters and / or rules that define various aspects of the CEEC market, including how and when the usage information indicates, and the CEEC mechanisms of the illustrative embodiments perform or operate Other to prescribe how to do Is a set of parameters and rules. A specific building block for facilitating CEEC market operations is a transaction, which may be used automatically, manually, or using a combination of automated and manual processes, such as by transaction builder 312 of FIG. And can be created.

  The CEEC market maker can assemble the transaction, but the individual owners of the buying and selling systems can also proactively participate in the transaction via the system usage manager 316. That is, if transaction builder 312 allows planners, designers, and managers to plan and / or record migration of a set of selling CEECs to a buying party, system usage manager 316 Allow individual users to participate in the market in connection with their individual systems designated as sellers. Further, if the transaction builder 312 proposes a set of transitions, in some cases, the user of such a system positively accepts an invitation to participate in such a transaction via the system usage manager 316. Will have to. In addition, the system usage manager 316 may allow a particular system user to be aware of his / her CEEC obligations, solicit a denial of CEEC by charging a different business use, and participate in future transactions. It is a mechanism that can inform the will.

  Since the CEEC market maker mechanism of the exemplary embodiments manages all the CEEC used in the CEEC market, such a CEEC market maker mechanism may allow a party to fulfill its obligations. Has the authority to invalidate the CEEC if it fails. This can be done by using your own allocated computing resources, or by invalidating your contract when using old hardware without good reason. Or provide the contracting party with the motivation to use the hardware in a manner that does not match the expectations defined by the CEEC. Thus, within the CEEC market mechanism of the exemplary embodiments, a seller can use an effective CEEC to use a computing resource by an organization or company that owns the computing resource allocated to it. It becomes a sale side because it does not have. Contract invalidation also works on the purchase side, and good new computing resources that are not fully used are invalidated for the contract and the resulting workload associated with CEEC and these CEECs. Force repurchase of additional work load from sale side by relocation.

Workload management based on CEEC The mechanisms of the exemplary embodiments relate to the usage of computing resources by the contracting parties, with the risk that the entitlement will be invalidated if the usage does not meet the minimum requirements. By expectation, the CEEC is manipulated to facilitate the association of the entitlement specified in the CEEC with the computing resource. As a result of CEEC management using the mechanisms of the exemplary embodiments, workload management is enabled. According to these CEECs and their association with computing resources, the workload is routed to the computing resources and the usage of the computing resources is monitored. Thus, when a workload is presented by a particular contracting party, the CEEC associated with that contracting party will determine which computing resources will be used to execute or otherwise perform the workload. Can be used as a basis for making decisions.

  The workload management mechanism 380 of FIG. 3 can control workload and route to computing resources according to the CEEC associated with the particular contracting party that presents the workload and the type of workload presented. . In one exemplary embodiment, upon receiving a workload from a contracting party's computing resources, one or more CEEC data structures associated with the contracting party can be retrieved from the CEEC database and presented workload. Type, for example, storage workload, processing workload, etc. can be determined. The type of business purpose associated with the retrieved one or more CEEC data structures can be determined and matched to the type of workload. As a result, a CEEC data structure directed to the same business purpose as the workload, and thus a CEEC data structure managing a type of computing resource capable of handling that type of workload, is identified. The CEEC data structure can then be used to identify the corresponding computing resource managed by the CEEC data structure, and then the workload management mechanism 380 is specified in the CEEC data structure Workloads can be routed to computing resources. For example, if a workload is presented to store data in the storage system, it is relevant to the storage system and is used in the manner described herein (eg, the cohort association described herein). (Using) the CEEC associated with the contracting party that presented the workload associated with the storage system computing resource, using the CEEC associated with the contract party, the computing to which the workload is directed・ Manage resources.

  Thus, although the exemplary embodiments do not perform workload management directly, workload management is a beneficial result obtained by the CEEC-based mechanism of the exemplary embodiments. For example, a request for a new “workstation” will be formulated as a request to execute a specified workload by some technical standard, ie, the ultimate new sale CEEC, with a specified pattern usage intensity by the CEEC. Let's go. This will be cohort on the basis of the intended business use and will be matched with a buyer with a usable capacity available.

As described above, one of the ways in which the market maker mechanism of the illustrative embodiments operates is in providing logic, such as by the cohort and profile manager 314 or cohort management service 324 of FIG. Generating and managing a cohort of computing resources and CEEC by applying profiles to CEEC computing resource usage data, conditions and condition information, and computing resources and CEEC configuration data It is. As a result, the cohort can function as a buyer and / or seller within the market maker mechanism of the illustrative embodiments.

  A cohort represents a collection of computing resources or CEECs or both that are similar in some definable way. For example, the same or similar configuration, the same or similar type, the same or similar organizational allocation (eg, related to a particular site or location of an organization), physical location, organization or company data Multiple computing resources, such as logical locations in the network, can be combined into the same cohort. Similarly, multiple CEECs having the same or similar conditions or subset (s) of conditions can be combined into the same cohort of multiple CEECs, for example, multiple CEECs managing storage capacity can be combined into one Multiple CEECs that can be collected in a cohort and related to contracting parties in the same or similar department or department of the organization or company can be collected in one cohort and have the same or similar business objectives Of CEEC can be collected in one cohort. A cluster analysis mechanism is used to identify these computing resources and CEECs so that such similar computing resources and CEECs can be combined into a collection or cohort of computing resources and CEECs. can do.

  For example, the cohort management service 324 analyzes the configuration information regarding the computing resources stored in the configuration management database 332 and the CEEC conditions identified in the items of the CEEC database 334 and performs cluster analysis on this information. Can generate one or more cohorts of computing resources and one or more cohorts of CEEC, where each cohort has members that have similar characteristics as other members of that cohort . “Similarity” refers to a wide variety of points, for example, the same or similar (ie, within a defined tolerance) number of processors, the same or similar amount of memory, the same or similar software configuration, The same or similar resource usage requirements, the same or similar business objectives, the same or similar organizational location, etc. can be measured. The specific rules and parameters used to establish a cohort are identified in the profile that defines the cohort and can be recorded in the CMDB as described below.

  Once the computing resource cohort is created and the CEEC cohort is created, the cohort management service 324 satisfies the same or similar conditions of the CEEC within the CEEC cohort (or simply the “CEEC cohort”). Computing the CEEC cohort based on one or more cohorts of computing resources (or simply “computing resource cohorts”) that provide computing resources determined to be necessary for There can be logic to match the resource cohort. In this way, entitlement contracts associated with the user can be associated with one or more computing resources to satisfy such contracts in an automated manner. Of course, the cohort information and associations between cohorts allow the cohort and profile manager 314 to allow the individual to review, cancel, modify, override, or otherwise change the cohort and its associations when desired. To the system administrator or other authorized users. The association of one CEEC cohort with one or more computing resource cohorts is essentially the computing resource cohort (s) useful to the contracting party to the CEEC within the CEEC cohort. A resource or at least a portion of its computing resource is reserved. Thus, once the CEEC cohort and computing resource cohort are associated, the CEEC enforcement logic takes effect.

  The cohorts that are matched and associated with each other in this way may be monitored and analyzed for their usage in consideration of the association between the cohorts. That is, computing resource usage for a computing resource that is a member of a computing resource cohort based on conditions specified in the CEEC of the CEEC cohort (s) associated with the computing resource cohort Information can be collected, analyzed and monitored. In essence, the collection, analysis, and monitoring of this information is performed to ensure that the terms of the CEEC are complied with by both the computing resource provider and the contracting party, eg, the end user. . Accordingly, the collection of usage information, its analysis and monitoring is performed with respect to the specific computing resource usage conditions specified in the CEEC of the CEEC cohort, but the collection is performed on the computing resource with which the CEEC is associated. Run from the computing resource specified in the cohort (s). Thus, for example, a CEEC may specify that a user must use his assigned processor for 50% of that time in order to execute a software package intended for accounting operations. If this is the case, analyze the processor usage metrics collected for the computing resources of the computing resource cohort with which this CEEC is associated and at least 50% of that time during the specified period , Will determine if you were running accounting software.

  It should be recognized that the collection of computing resources and CEECs into a cohort need not be limited to a collection based on computing resource configurations or CEEC conditions as a whole. Conversely, for example, a sub-part of one CEEC can be individually assigned to different CEEC cohorts based on the CEEC conditions associated with that sub-part. For example, the CEEC may specify conditions related to allocation of processor resources, storage system resources, web application resources, etc. and use by contracting parties. Each of these may be able to be individually satisfied by different computing resources of the organization or enterprise, and therefore separate one CEEC into separate CEEC cohorts so that they can be associated with multiple CEEC cohorts Each of these associations with individual CEEC cohorts can be managed individually by the mechanisms of the exemplary embodiments and can be individually targeted for CEEC market maker operations.

  Similarly, cohort membership can be based not only on similar conditions in CEEC or similar configuration of computing resources, but also on similar usage metrics. That is, two computing resources may have a similar configuration, and therefore, even if they are usually considered to be part of the same cohort, these computing resources are either in significantly different ways or at different levels or If the usage metrics indicate that it is being used at the usage intensity of the pattern, these two computing resources may be separately categorized into different cohorts. The same is true for CEEC if the usage metrics associated with that CEEC can be used as a basis for determining whether a CEEC should be classified into a particular cohort. Usage metrics can be obtained in any known manner, such as using an agent application that runs as a daemon process on the computing resource, or exploring the computing resource. Usage metrics are collected by a data collection mechanism such as the information technology monitoring warehouses 352-356 of FIG. 3, and analyzed and used, for example, generating usage scores via usage scoring service 326. Status information can be generated. This usage information can be associated with various computing resources and CEECs via the database system 330 and is used by cohort and profile management mechanisms such as the cohort management service 324 to dynamically cohort membership. And re-coordinate cohort membership according to changes in computing resources and CEEC usage. Similarly, changes in the configuration of computing resources are identified by database system 330 and can be used to move cohort membership when deemed necessary to optimize the computing resource usage of an organization or company. Can be used as a basis to adjust automatically.

  The usage scoring service 326 stores in the configuration management database (CMDB) 332 and / or the CEEC database 334, as described in more detail below, for each metric in the cohort for metrics of interest, and You can operate on the collected usage metrics, such as generating a statistical expression of interest for that cohort specified in the profile that defines the particular cohort. These scores are defined in a cohort profile that can be generated according to usage service logic rules and parameters, or can be static or dynamically determined weights, as also described below. Weights, or both. The resulting score provides a relative measure of the usage of a member of the cohort relative to other members of the cohort. This usage is used to generate an overall representation that indicates whether computing resources are being used according to CEEC conditions or whether the CEEC conditions are satisfied by the analyzed computing resource usage. It can be compared to the requested usage specified in the conditions of the CEEC with which the members of the computing resource cohort are associated. The resulting score can be used as a basis for cohort membership adjustment, CEEC negation, replacement, or transition, or combinations thereof.

  These score representations can be generated, for example, by the cohort and profile manager 314 and presented to authorized users. For example, a graph representation of a CEEC score for a CEEC cohort is presented, such as in a multi-axis graph, along with the scoring of computing resources for one or more computing resource cohorts with which the CEEC cohort is associated. As a result, it can be determined which CEECs are satisfied by the use of computing resources and which computing resources are used to meet the CEEC conditions of the CEEC cohort. To assist in the visualization of this information, including the use of red / yellow / green color coding, for example, various graph representations are used to further identify and provide compliance information and reports regarding the conditions of the CEEC within the CEEC cohort be able to. Such a graphical representation can be provided, for example, on a per-CEEC basis, on a CEEC cohort basis, or on a set of CEEC cohorts.

  FIG. 5 is a flow diagram outlining an example operation for generating a cohort association according to an exemplary embodiment. The operations outlined in FIG. 5 may be performed, for example, either by cohort management service 324 alone or in combination with, for example, the cohort and profile manager 314 of FIG. FIG. 5 is an overview and many details specific to a particular implementation of exemplary embodiments may not be explicitly shown in FIG. 5, but are easy for the party in view of this description. Recognize that it will be self-evident.

  As shown in FIG. 5, this operation may define one or more cohort profiles, at least one for designating a computing resource cohort and one for designating a CEEC cohort. (Step 510). Applying the defined cohort profile to computing resource configuration information and CEEC information in the database system, at least one computing resource cohort having one or more members, and at least one Generate at least one CEEC cohort with members (step 520). Note the cohort membership of the CEEC and computing resources and store (530) in the database system in association with items relating to the computing resources and CEEC. As described above, a single CEEC can be part of multiple CEEC cohorts, such as when a single CEEC condition is subdivided. Similarly, a computing resource can be part of multiple computing resource cohorts by subdividing its configuration information into individual types of configuration items. Thus, a CEEC cohort may be associated with multiple computing resource cohorts and vice versa.

  The common or similar condition of the CEEC cohort (s) is then compared against common or similar configuration information about the computing resource cohort to compare one or more computing resource cohorts to one Match one or more CEEC cohorts (step 540). Associations between cohorts can be created and these associations can be saved for future use (step 550). Based on these associations, a computing resource or a portion of the computing resource is reserved or allocated to a CEEC in the associated CEEC cohort (step 560).

  Computing resource usage metrics are collected from various computing resources and stored in the database system in raw form, as analyzed usage information, or both (step 570). Analyzing computing resource usage information in the database system based on the cohort association and its usage with respect to the computing resource in the computing resource cohort (s) with which the CEEC is associated To determine whether the terms of the CEEC in the CEEC cohort are being followed by the contracting parties (step 580). Based on a determination of compliance with the CEEC conditions, a graphical representation of the compliance report can be generated (step 590) and the cohort membership can be updated (step 595). Thereafter, this operation ends.

  Accordingly, exemplary embodiments provide a mechanism for reserving computing resources of a data processing system in a data processing system that includes at least one computing device and a plurality of computing resources. . These mechanisms can establish one or more computing environment entitlement agreement (CEEC) data structures, each CEEC data structure between the contracting party and the computing resource provider of the data processing system. The terms of the business level agreement are defined and the terms of the CEEC shall be used by the contracting parties for the specified purpose at the specified level and pattern intensity for the specified period. Specifies a set of computing resources having These mechanisms can further associate one or more sets of computing resources with one or more sets of CEECs. Furthermore, the mechanism can manage a set of one or more computing resources according to an associated set of one or more CEECs. Responding to contracting parties failing to use computing resources within a selected computing resource cohort for a specified purpose, at a specified level and pattern strength for a specified period of time Thus, the logic of at least one computing device disables CEEC. As a result of such invalidation, the CEEC may be replaced with other CEECs, or invalid CEECs may be migrated from one set of computing resources to another, or both. .

  The relationship between resources, workload, and CEEC can be very complex as described above, or it can be very simple. It is important to note that simple CEEC can be introduced into more complex environments through trading. For example, the CEEC may require a standalone desktop workstation to be used for "basic office productivity" (email, word processing, etc.). This CEEC can be deployed in a very complex and centralized virtualized cluster with transactions, in which case the same business activities will be run on a very complex platform, which is very different for the user Would be used in the same way to do the same thing as on a standalone desktop workstation. This can vary from an environment in which you have complete control to an environment where you have limited or no control at all, an environment that you do not understand, and an environment that may be operated by someone you do not know It underscores why CEEC is required to provide users with confidence that their requirements are protected by this process of moving to a geographical environment or the like.

  The association of the one or more sets of computing resources with the one or more CEECs is one of the plurality of computing resources based on configuration information about each of the plurality of computing resources. Or determining a first set of computing resource cohorts. Multiple computing resources within a computing resource cohort can be of similar types to each other and can have similar configurations to each other. This association may include determining a second set of one or more CEEC cohorts comprising a plurality of CEECs based on attributes of one or more CEECs maintained by the data processing system; The selected CEEC cohort and the selected computing resource cohort based on the conditions of the CEEC within the selected CEEC cohort within and the characteristics of the selected computing resource cohort within the first set Further matching can be included.

  Managing a set of one or more computing resources according to an associated set of one or more CEECs is within a selected computing resource cohort according to the conditions of the CEEC within the selected CEEC cohort. Enforcing the CEEC on the computing resource can be included. When the CEEC enforcement obtains the resource usage metric information, determines whether the CEEC conditions are complied with based on the computing resource usage metric information, and compares it with the conditions in the CEEC Based on actual computing resource usage, deny CEEC, replace CEEC, or from one CEEC cohort to another CEEC cohort or from one computing resource cohort to another computing resource cohort Determining whether to migrate the CEEC or a combination thereof can be included.

Associating CEECs and Cohorts with Existing Organizational System Information CEECs, cohorts, etc. can be associated with existing organizational information such as employee information, departments, departments, and location information, business units. For example, database system 330 interfaces database system 330 with these existing organizational systems, thereby obtaining additional information from these existing organizational systems, or CEEC-based resources. One or more organizational system interfaces 336 may be included to link the CEEC, cohort, and other data structures of management system 300 with these existing organizational systems, or both. In an exemplary embodiment, the contracting party may be an employee or department of an organization or company, and identification information about these employees or departments may include employee directories, organizational configuration systems, human resource systems, etc. Can be obtained from an organizational system 340 that can include:

  Information obtained from the existing organizational system 340 is entered into the fields of the items of the CEEC database 334 and / or the CMDB 332 so that information from these other organizational systems 340 can be entered into the CEEC and computing resources and It can be used by database system 330 to associate with those cohorts. For example, a CEEC can be assigned to a particular employee, and that employee's information is automatically retrieved from an existing organizational system 340, eg, a human resources system, and an entry in the CEEC database 334 for CEEC, eg, Can be used to fill in fields such as employee identification number, name, department, business unit. Similarly, to link information from the database system with items relating to the contracting party, eg, the ID of the CEEC associated with the employee, the ID of the computing resource cohort associated with the CEEC associated with the employee, etc. The existing organizational system 340 can be updated.

  Moreover, the information obtained from the existing organizational system 340 is used by the cohort management service 324 and the cohort and profile manager 314 to help identify the members of the cohort, i.e., to help generate the cohort. be able to. For example, profiles can be generated for specific business units, departments, or other organizational units within an organization or company, using information obtained from an existing organizational system 340 to generate these profiles, Can be entered in the corresponding cohort.

  Another example of how these organizational systems 340 can be used to assist in the creation and management of cohorts is to obtain and analyze computing resource usage information for various cohorts and relate them to computing resources. Other cohorts with similar usage trends and patterns that can identify usage trends and patterns based on the CEEC to which the organization information from organizational system 340 is generated Other contract parties, business units, etc. that can be associated with This information can then be used to determine cohort membership between such organizational entities, such as employees, business units, departments, and the like.

Profiles for defining a profile cohort are, for example, one or more user interfaces provided by the cohort and profile manager 314 of FIG. 3 alone or in conjunction with the logic of the cohort management service 324, Can be defined by a system administrator or other authorized individual. Profiles can be generated to define various cohorts of computing resources and / or CEEC. The profile specifies the parameters used to define the cohort as well as which computing resources / CEEC can be a member of the defined cohort. Profiles evaluate which usage metrics are evaluated to determine membership within a cohort and which levels and / or patterns of usage associated with those metrics are removed from membership, cohorts within a cohort Can be specified. As described below, these usage levels and / or patterns for adjusting membership within a cohort can be determined based on generated usage information, for example, selection of cohort model and reverse model members. Can be determined dynamically.

  Profiles can be applied to computing resource configuration information (eg, available from CMDB 332) and CEEC data (eg, available from CEEC database 334) and usage corresponding to one or more metrics. Regardless of whether the information is raw metric data or the result of processing raw metric data, for example, usage scores, either database can store this usage information. Profiles can be applied to computing resource configuration information and CEEC data to identify which computing resources and which CEEC should be members of the corresponding cohort. The database system 330 can be updated accordingly to include the ID of the cohort with which the particular computing resource and CEEC are associated.

  Profiles can be defined in a wide variety of ways depending on the particular implementation. Preferably, as shown in FIG. 6, the profile 600 includes a unique ID 610 of the profile, a computing resource characteristic indicating membership within the cohort corresponding to the profile or a CEEC condition 620, within the corresponding cohort. Identification information 630 of specific usage metrics that are to be used to evaluate membership of the metric, criteria 640 for evaluating these metrics, criteria for determining when to adjust membership in the corresponding cohort 650, corresponding actions 655 associated with these adjustment criteria, other parameters of the profile and rules 660. The criteria 640 for evaluating metrics to determine membership within a cohort include, for example, a time frame for performing the evaluation, a basis for measuring usage (eg, maximum, minimum, average, etc.), this Such evaluation biases (eg, best, worst, latest, moving average, etc.), or a combination thereof.

  Adjustment criteria 650 and corresponding action 655 can specify a range of metric usage information that, for example, results in a need to perform a particular action. These actions 655 may decide to deny the CEEC / CEEC cohort by a CEEC market maker action, eg, buy, sell, hold, or retire, or otherwise Refers to adding more CEECs to a computing resource or cohort, selling refers to moving a CEEC from a computing resource or cohort, holding means no action is taken, and discarding is Means that the computing resource or cohort is permanently discarded so that it is not reused. Accordingly, the CEEC market maker service 322 can use the profile as a mechanism to assist purchaser and seller selection within the CEEC market managed by the CEEC market maker service 322.

  The cohort management service 324 is a profile 600 generated by a system administrator or other authorized user so that it can be applied to the computing resource configuration information in the CMDB 332 and the CEEC conditions specified in the CEEC stored in the CEEC database 334. Can be stored. First, to determine membership in the cohorts defined by the various profiles 600 when the system initializes, when new computing resources are added to the system, or when a new CEEC is created. There will be no usage information in the database system 330 to use as a basis for. Accordingly, membership within the cohort can be initially determined based on computing resource configuration information and CEEC conditions specified in the database system 330. Thereafter, as computing resources are used based on various CEECs, usage information is collected / generated and the computing resources in the various cohorts defined by this profile are subject to the adjustment criteria specified in these profiles 600. And can be used as a basis for dynamically adjusting membership of CEING resources and CEEC. Accordingly, the cohort management service 324 can monitor the profile continuously or periodically and apply the profile to information stored in the database system 330 to determine adjustments to the cohort membership.

  Profiles 600 created and managed via the cohort and profile manager 314 and stored in association with the cohort management service 324 are via one or more user interfaces provided by the cohort and profile manager 314. It can be updated by authorized users. In addition, these profiles 600 are based on analysis of usage metrics for cohort members, eg, usage metrics distribution, representative cohort members, outlier members, etc., as described below. Different usage metrics or different weights for use to evaluate cohort behavior based on the identification information may be selected and automatically updated. In response to these profiles 600 being updated, within the database system 330 for other computing resources and / or CEEC that may be candidates for inclusion in the cohort considering the updated profiles 600 With the configuration information and CEEC information, the updated profile 600 can be applied to existing members of the cohort by the cohort management service 324 to determine whether membership should be maintained or changed.

Using a profile for scoring usage information to manage a cohort As noted above, the profile 600 specifies computing resource usage information to thereby assess the usage of cohort members. Can do. Each member of the cohort is evaluated according to the same profile 600 that defines the cohort. Such an assessment can be performed, for example, either by cohort management service 324 alone or in combination with cohort and profile manager 314, either automatically or by manual intervention by an authorized user. This rating was specified in profile 600 when applied to computing resource usage information available from database system 330 that can include usage scores generated by usage scoring service 326. It can be executed based on rules, parameters, etc.

  For example, profile 600 may show that for a particular cohort, the most important usage information to use to evaluate the members of that cohort is the storage capacity metrics, processor cycle statistics, average number of processes performed for a given period of time. Can be specified. This computing resource usage information can be obtained, for example, from the database system 330 or the usage scoring service 326 or both. Usage information can be used to “score” members of a cohort and / or the entire cohort according to scoring rules and weights. Such scoring can be performed, for example, by the usage scoring service 326 of FIG. The resulting scores can be categorized into predefined categories of resource usage scores that indicate the relative usage of computing resources based on the associated CEEC of the CEEC cohort where usage scoring is desired, For example, the resource usage is within a range indicating that the computing resource or cohort is being used according to the CEEC condition, is within an acceptable tolerance of the CEEC condition, or is acceptable of the CEEC condition. Scores can be categorized into “green”, “yellow”, and “red” categories that indicate whether they are within. When the profile 600 is updated, the scoring of cohort members or other computing resources / CEECs or both that may become members of the cohort will include the modified resource usage information parameters, weights in the updated profile Based on the above, it can be updated in the same manner.

  For example, this categorization base is green (indicating that no further usage is reasonably predictable for this set of resources), red (these resources are not being used), and yellow (red and green Usage between thresholds that define the categorization of). These categorizations can then be associated with corresponding actions that are expected to be performed, which actions may include CEEC negation, CEEC replacement, CEEC transition, and the like.

  For example, a profile for a CEEC cohort may specify criteria for assessing computing resource usage based on that cohort's CEEC (again, a cohort member can define its cohort definition). (Note that it is evaluated according to the same profile you specify). These metrics are based on the measurement basis (eg, minimum, maximum, average, volatility, etc.), time scale (eg, time unit) for each computing resource metric specified in the profile , Daily, weekly), scoring time frame (eg, days to consider), and any scoring bias (eg, best, moving average, latest). These metrics include a method for interpreting and processing computing resource usage metric measurements to reach a single score indicative of computing resource usage, and computing resource usage for CEEC conditions in the CEEC cohort. Specify how the situation is measured. These criteria are used to determine how to interpret the computing resource usage data and apply it to decisions regarding whether the CEEC terms are being complied with by the contracting party and the computing resource provider. Can be set according to the business goals of the organization or company.

  Assessments can be used directly or aggregated, and accepted usage patterns may be identifiable within a given time scale or within a set of multiple instances of a given time scale Means that. For example, an assessment can be performed for acceptable usage at least one day per week in a time frame of the last six weeks.

  The scoring operation performed by the usage scoring service 326 can be performed in various ways based on the evaluation criteria specified in the profile for that cohort. The particular function or mathematical algorithm used to generate the score is not the focus of the present invention, it can take the evaluation criteria specified in the profile and can be a single for CEEC and / or computing resources Any suitable function or mathematical algorithm capable of generating a unified score is intended to fall within the spirit and scope of the exemplary embodiments.

  In some exemplary embodiments, these functions and / or mathematical algorithms are applied to a variety of computing resource usage metrics and / or metrics specified in the profile. Weighting can be used. These weightings can be constant and predefined, for example, CPU usage has a weight of 1.0, storage capacity usage has a weight of 1.25, and average weekly bandwidth usage The situation has a weight of 2.15. These weightings can be set to any desired value to achieve the business goals of the organization or enterprise with respect to CEEC and computing resource management.

  Instead, these weights can be determined dynamically, can be specific to a particular profile and cohort, and different profiles and cohorts use different weights for the same computing resource usage metrics can do. These dynamically determined weights are based on statistical analysis of computing resource usage metrics collected for the computing resource cohort (s) with which the CEEC of the CEEC cohort is associated. Can be further determined. That is, a statistical analysis of computing resource usage metrics with respect to computing resources can be used to analyze specific computing resources, such as CPU capacity, storage capacity, memory capacity, bandwidth, etc. It may indicate that the actual relative usage of the computing resource tends to be stronger when compared to the resource, complying with the conditions of the CEEC with which the computing resource cohort is associated May more strongly indicate whether the computing resource is being used, and thus the weighting of this computing resource usage metric is determined by this CE for the computing resource cohort (s). It can be increased dynamically to other computing resource usage metrics are monitored on the basis of the profile of C cohorts. Thus, statistical analysis of computing resource usage metrics can be used to generate a performance score for these computing resource usage metrics when generating a score for a computing resource, a computing resource cohort, a CEEC, or a CEEC cohort. Can be used as a basis for determining the dynamic weights.

  In addition, the weighting ratio allows for the detection of usage patterns that do not meet expectations for the assigned cohort or that meet the expectations of some other cohort. In some cases, a usage pattern with an unbalanced ratio, such as a combination of low RAM usage and 100% CPU usage, indicates a system problem. The threshold defined in the profile determines how such usage should be interpreted in relation to the cohort.

  Weighting can be used as a mechanism to ensure that a profile is suitable for a deterministic limit on the computing resources of a cohort defined by that profile. Thus, for example, in a computing resource whose usage is primarily limited by the amount of available memory resource, the weighting for the memory resource may be other than having the computing resource where the memory resource is not as limited. May be heavier than the cohort. Thus, the weighting can be customized in each profile so that it can be set appropriately for a particular set of computing resources and CEEC, including the cohort defined by that profile.

  In view of the above, exemplary embodiments enable the use of computing resources for a computing environment entitlement agreement in a data processing system that includes at least one computing device and a plurality of computing resources. It can be appreciated that it provides a mechanism for monitoring. These mechanisms establish one or more computing environment entitlement agreement (CEEC) data structures, each of which defines the terms of a business level agreement between the contracting party and the data processing system provider. A CEEC condition defines a set of computing resources having a specified configuration that is to be used by a contracting party for a specified purpose at a specified level and pattern strength for a specified period of time. Is specified. These mechanisms further generate a CEEC cohort that includes a set of CEECs with similar conditions and a set of computing resources with properties that satisfy the similar conditions of CEEC within the set of CEECs. In addition, these mechanisms measure the usage of each computing resource in the set of computing resources within one or more computing resource sets or cohorts according to similar conditions of the set of CEECs. Collect resource usage metrics. In addition, the mechanisms of the illustrative embodiments provide a relative weighting of resource usage metrics for various of the computing resources within the set of computing resources based on the business goals defined for the CEEC cohort. decide. Moreover, the mechanisms of the illustrative embodiments report the CEEC cohort usage of a set of computing resources based on the collected resource usage metrics and the relative weights determined for the resource usage metrics.

  FIG. 7 is a flow diagram outlining an example operation for generating a computing resource usage score in accordance with an illustrative embodiment. The operations outlined in FIG. 7 may be used, for example, when scoring usage metrics for computing resources within a computing resource cohort and / or a CEEC cohort, for example, usage scoring service 326. Can be executed by.

  As shown in FIG. 7, this operation begins with usage scoring service 326 retrieving a profile for the cohort that is the subject of the usage scoring operation (step 710). Evaluation metrics and evaluation criteria information for the cohort is extracted from the retrieved profile (step 720), and usage metric information for the computing resource (s) associated with the cohort is extracted or collected (step 730). . This retrieval may, for example, retrieve usage metric data from the database system 330 if the data has already been collected from the information technology management systems 352-356, or a particular computing resource. In some cases, usage metric data may actually be collected from information technology management systems 352-356.

  A weight to be applied to the retrieved / collected evaluation usage metrics is determined (step 740). This can be done, for example, by retrieving static predefined weights that can be specified in the profile 600. Alternatively, this weighting can be determined dynamically by analysis of trends and statistics from usage metrics previously collected and analyzed for this particular cohort. If the weights are determined dynamically, these weights can be stored in the fields of profile 600 for retrieval by usage scoring service 326 as well, but can also be updated dynamically. Regardless of the specific implementation, this weight is specific to the cohort that is scored, and for either the same type of usage metrics or other types of usage metrics, other weights for other cohorts are It may be different from the weighting for the current cohort.

  This weighting is applied to the corresponding usage metric (step 750) and a score is calculated based on the weighted usage metric and the evaluation criteria specified in the cohort profile 600 (step 760). As described above, the evaluation criteria include the time frame for performing the evaluation, the basis for performing the evaluation (eg, maximum, minimum, average, etc.), and the bias for such evaluation (eg, best, worst, latest, Various criteria can be specified for evaluating weighted usage metrics, including moving averages, etc.), or combinations thereof. These can be applied to usage metrics along with weighting using one or more mathematical algorithms or formulas to generate a single score for that cohort or members of the cohort. The score may be determined directly within the time scale, or may be aggregated for each time scale within the time frame, for example, the most recent daily score or the daily score for each day within the time frame. is there.

  The score is then compared with one or more thresholds to categorize the cohort usage for whether the usage complies with the cohort usage requirements (step 770). The score (s) generated in this way can be stored in association with items corresponding to the cohort in the database system (step 775), and categorization of usage based on scoring A determination is made as to whether to indicate the need to be performed and which action to perform based on the categorization (step 780). These cohort management operations, for example, purchase, sell, hold, or remove the CEEC associated with that cohort, launch tickets to add additional computing resources to the cohort, and For example, a ticket for abolishing computing resources can be activated. The determined cohort management action can then be initiated (step 790), for example, via the CEEC market maker mechanism 332, and the operation ends.

Cohort Model Member Selection In addition to the above, scoring performed by the usage scoring service 326 in accordance with the profile 600 and computing resource usage information selects one or more model members of the cohort. Can be used as a basis for. For example, a list of members of a cohort can be provided and ordered by score, then a cohort model member, for example, a member representing the average score of the members of the cohort, having a score closest to the overall score for the entire cohort Members can be selected from the list. A model member (typically a member in a cohort with computing resource usage deemed good / acceptable and achievable within the business context) is an acceptable usage boundary for other members within the cohort Used to describe The exemplary members can be automatically selected by the cohort management service 324 using the selection criteria and the ordered list of members, or can be presented to the user through the ordered list of members. Can be selected manually via the cohort and profile manager 314 using a graphical user interface that can select model members from the list.

  Similarly, reverse model members can be selected in a similar manner, either automatically or manually. The reverse model member of the cohort is preferably selected from members in the cohort that are not used in the business context, for example, computer resource usage information in the “red” category is other members in the cohort Used to describe unused boundaries. Such reverse paradigm defines the maximum amount of background or baseline activity that should be considered “noise floor” of the cohort, ie, “not used”. For example, in the case of RAM, the reverse model defines the amount of RAM needed to load the OS without doing anything else.

  The cohort model and reverse model members correspond to the boundaries in profile 600 corresponding to that cohort, for example criteria 650 for determining when membership in the corresponding cohort should be adjusted and the corresponding criteria associated with these adjustment criteria. It can be used as a basis for setting action 655. Thus, for example, based on specific usage information that is important for a particular cohort, a statistical representation of the actual usage information collected for members of that cohort can be generated, for example, usage related to metrics of interest. A distribution of status information, normalized curves, or other statistical representations can be generated for the cohort. This statistical representation can be based on the weights provided to the various elements of the usage metrics, and as previously mentioned, the weights are predefined or determined dynamically. In addition, statistical representations can be generated based on usage information obtained for a particular time period, and therefore, statistical representations can represent trends in computing resource usage behavior within a cohort over time. it can.

  The resulting statistical representation automatically identifies the cohort model and reverse model members for use in setting the upper and lower limits of computing resource usage to account for membership in that cohort Can be analyzed by the cohort management service 324. Alternatively or additionally, graphical output of statistical expressions via cohort and profile manager 314 so that authorized users can select model and reverse model members or override the automatic selection made by cohort management service 324 Can be generated and presented to authorized users.

  The generation and analysis of statistical representations of resource usage information performed by the cohort management service 324 can be used as a method of comparing multiple cohorts with each other. That is, trends (patterns) and statistical representations of one cohort can be compared with those of other cohorts to determine which cohorts have similar resource usage trends. When you need to change a cohort membership, you can first check between logically connected cohorts to determine if a cohort member should be transferred to a logically connected cohort These similar cohorts can be logically connected within the cohort management service 324. In this way, using one type of “linked list” approach, we first look for what is logically linked directly to the first cohort, and then logically link to the cohort that is directly linked to the first cohort. A new cohort to which a member to be evicted from the first cohort can be relocated can be found, for example, by continuing to search for a cohort that is connected to the first cohort. Such cohort reassignment could be performed on a cost and / or value basis, especially if the costs of cohorts with similar usage patterns or trends are significantly different.

  A statistical representation or other analysis of resource usage information can be further used to determine the members of the cohort who are outliers and must be evicted from the cohort. That is, a member of a cohort with a score or computer resource usage information that indicates that it is significantly different from a cohort model or reverse model member (eg, can be determined to be significantly different based on a predetermined threshold) Can be identified as outliers and as candidates for eviction from the cohort. By eviction, the member is no longer associated with the cohort and is therefore not included in the assessment of the details of the cohort, but the member is also more appropriate through the market maker mechanism as described below. Associated with a cohort. In essence, the eviction causes the current cohort to become the CEEC or computing resource seller and other cohorts to function as potential buyers of that CEEC or computing resource. The buy-side and sell-side choices and use of market factors in CEEC-based market maker mechanisms such as CEEC market maker service 322 are described in more detail below.

  FIG. 8 is a flow diagram outlining an example operation for selecting cohort paradigms and reverse paradigm members according to an exemplary embodiment and using this selection to define metrics and related actions in the profile. . The operations outlined in FIG. 8 may be implemented by the usage scoring service 326 along with the cohort management service 324, for example.

  As shown in FIG. 8, this operation begins with obtaining usage scores for members of the cohort, which can be computing resources or CEECs (step 810). Depending on the score associated with the members of the cohort, an ordered list or other ordered array of the members is generated and ordered (step 820). A cohort exemplary member is selected from the ordered sequence of members based on exemplary selection criteria, eg, average, closest to average score, etc. (step 830). A cohort reverse model member is selected from the ordered sequence of members based on the reverse model selection criteria, eg, the furthest score from the mean, the highest or lowest score, etc. (step 840). Based on the usage metrics of the exemplary members, usage boundaries for the cohort, such as thresholds and usage ranges or score ranges, and associated actions are set (step 850). Thereafter, this operation ends.

Computing Resource Reservation Based on Association of CEEC and Cohort Computing of a cohort for use by CEEC contracting parties within a CEEC cohort associated with a computing resource cohort based on an established cohort and its association -Various resources are reserved. The CEEC-based reservation service 328 performs such reservations within an organizational or enterprise system and removes reservations for computing resources, such as part of the transaction settlement phase of a transaction, as described below. Contains logic to do. Such reservations include generating user accounts, transferring user accounts, and taking receipt of computing resources such as laptop computers, desktop computers, etc. to contracting parties. Generating computing r resources from contracting parties, for example, generating collection tickets for collecting laptop computers, desktop computers, etc. that are not used or not fully used from contracting parties Can be included.

  The reservation of a computing resource is whether the computing resource is available for reservation by other CEECs of the same or different CEEC cohorts and how much of that computing resource is available Further comprising monitoring the total amount of computing resources of the computing resource cohort that is eligible for the CEEC counsel specified in the CEEC cohort associated with the computing resource cohort to determine be able to. For example, it can be determined that the CEEC of the CEEC cohort currently associated with the computing resource cohort represents 90% of the CPU computing resources of the reserved computing resource cohort. Based on the established computing resource reservation threshold, this amount of reserved computing resources can be compared to the threshold to determine if additional CEEC can be addressed, and the cohort Such decisions can be used when determining management operations, eg, CEEC and cohort associations, computing resource and cohort associations, CEEC transitions between cohorts, and the like. In addition, comparison with such thresholds requires the addition of additional computing resources to the organizational or enterprise system to increase the amount of available computing resources that can be used to associate with the CEEC. It can be used as a way to generate alerts or notifications to system administrators or other authorized users.

  Accordingly, based on the generation of CEECs, the generation of profiles, the generation of cohorts based on these profiles, and the association of CEEC cohorts with computing resource cohorts, computing and computing for use by these CEEC contracting parties. Resources can be reserved. In addition, whether sufficient computing resources are available to support the association of CEEC with cohort computing resources, whether CEEC migration is appropriate, or support CEEC for the CEEC cohort The system administrator or other authorized person regarding the need to add additional computing resources in order to do so, as well as where such additions are appropriate, ie which computing resource cohort is appropriate A determination can be made as to whether or not to notify the individual or a combination thereof.

  In addition, if the computing resources are no longer associated with a particular CEEC, the reservation of these computing resources by the CEEC can be removed. In cases such as moving a CEEC from a computing resource that needs to be retired, the CEEC-based reservation service 328 can be used by a system administrator or other authorized user to find that computing resource from a useful state within the organization / enterprise. Work tickets or notifications can be generated and issued in order to remove the physical hardware device in the case of a physical hardware device. It may be necessary to reconfigure the organizational / enterprise system, physically disconnect the hardware device, and remove and dispose of the physical hardware device. This is an old, potentially obsolete, used, for example, that is not being used, not being fully used, or used for a purpose different from that intended to be used as specified by the CEEC. May fall under the scenario of migrating CEEC from multiple computing resources and migrating CEEC to newer, potentially more efficient computing resources.

Determining CEEC Compliance With the mechanism described above, the CEEC-based market maker system 300 in the exemplary embodiments allows the computing resources to be used by the contracting party in compliance with the CEEC associated with the contracting party. Can be determined. In particular, the logic provided by the CEEC-based market maker system 300 manipulates the CEEC cohort, and the CEEC conditions within the CEEC cohort are the computing resource cohort (s) associated with these CEEC It can be determined whether the contracting party's usage of computing resources within is satisfied. The computing resource usage metric information obtained from the information technology monitoring system 350 and stored in the database system 330 and the scoring information obtained from the usage scoring service 326 are used to compute the computing resource It can be determined whether these computing resources in the cohort are being used to meet the predetermined usage requirements specified in the CEEC of the CEEC cohort.

  As an example, to simplify this example, assume that a CEEC cohort consists of a single CEEC and that this CEEC cohort is associated with a single computing resource cohort that includes multiple computing resources. This association between the CEEC cohort and the computing resource cohort can be performed according to the mechanism described above. Perform CEEC compliance operations by the CEEC manager 318 on the CEEC identified in the CEEC database 334 periodically, continuously, or in response to an event such as a system administrator or authorized user requesting a CEEC compliance decision. be able to. As a result of the CEEC compliance operation, a compliance report can be generated and output to the system administrator or other authorized user, and adjustments to the CEEC cohort can be initiated.

  The CEEC compliance action determines the minimum usage requirement specified for the CEEC for the CEEC cohort (or for each CEEC within a CEEC cohort in the case where there are multiple CEECs in a CEEC cohort). For example, 50% of the average CPU usage per day is devoted to running accounting software. Based on the profile associated with the CEEC cohort, the CEEC compliance behavior is computed for a particular computing resource of the computing resource cohort (s) with which the CEEC cohort is associated. -Computing resource usage information can be obtained from metric information. For example, a CEEC cohort is associated with a computing resource cohort corresponding to a particular type, number of cores, speed, etc. processor that can be specified in the profile of that CEEC cohort, and the number of processor cycle peaks per minute If the CEEC cohort profile indicates that a resource usage assessment should be performed on the basis of processor cycle peak number information collected per minute for the processors in the computing resource cohort, Computing resource information can be determined. This information can be correlated with information indicating which process was executing during this peak cycle number. This allows access to the length of time and the number of processor cycles that the computing resource has been devoted to performing various processes.

  The specificity of this review can vary depending on the cost of the CEEC. For example, the most expensive CEEC can be evaluated at this process level, and the cheapest CEEC can be measured more simply on the basis of average general usage. In the latter case, the compliance check only determines whether the weighted composite usage score is above a threshold. Any degree of specificity between two extreme values can be supported.

  Based on the computing resource usage information and the evaluation criteria specified in the CEEC cohort profile, a determination is made as to whether at least 50% of the daily CPU usage is devoted to running the accounting software. Thus, for example, computing resource usage information can determine the total number of peak cycles that have been devoted to the execution of accounting software processes as opposed to other types of processes. As a result, an average or other evaluation standard designation value can be generated for a designated period, for example, one day. This value is then compared to the minimum requirement specified in the CEEC conditions, and the CEEC is observed by the contracting party's use of the computing resource within the computing resource cohort with which the CEEC is associated. You can judge whether or not.

  Compliance can be determined based on strict or flexible compliance standards. Strict compliance requires that certain conditions of the CEEC are met, otherwise the CEEC is determined not to be compliant. Flexible compliance is considered to be in compliance with CEEC, but allows for tolerances around CEEC conditions that may be flagged for additional monitoring, for example. Thus, if a certain CEEC is flagged for additional monitoring, for example, events related to initiated CEEC compliance operations can be scheduled at a high frequency. Regardless of strict or flexible, based on the decision to comply with CEEC conditions, generate a representation of compliance and determine the appropriate action to be taken along with the actions defined in the profile in relation to the profile of the CEEC cohort Can be used as a basis for. For example, as described above, whether or not CEEC is observed or within the strict compliance tolerance range, but further monitoring or non-compliance must be identified by color coding. Graph representations such as axis graphs can be generated. This graphical representation can be output to a system administrator or other authorized user so that decisions regarding how to change the CEEC, cohort associations, etc. can be made to achieve the business goals of the organization or company.

  This may depend on the company's willingness and / or ability to handle the transition. For example, if the company can move 100 CEECs in a month, identifying 10,000 CEECs has little meaning. Thus, the threshold is initially defined as “loose” so that only the least compliant CEEC is identified and can then be defined more strictly as it progresses.

  Moreover, the actions specified in the profile for the CEEC cohort corresponding to various degrees of compliance can be automatically initiated using the CEEC market maker service 322. These actions can be, for example, sold, held, or eliminated. For example, if the compliance indicates that the CEEC is being complied with or within strict compliance tolerances, then a pending action can be taken that does nothing for the CEEC transition, so the CEEC can It remains part of the CEEC cohort, and the CEEC cohort remains associated with one or more computing resource cohort (s). If the CEEC is not compliant, but the compliance indicates that some usage based on the CEEC condition exists, initiate a sell action to deny the CEEC and set a new condition that corresponds to the actual usage. Can be replaced with a new CEEC that has, or the CEEC or replacement CEEC is migrated to one or more other computing resource cohort (s), or both. If the degree of compliance indicates that there is no usage for that CEEC condition, then the action may eliminate the CEEC without replacing the CEEC.

  It should be appreciated that similar compliance decisions can be made regarding compliance with computing resource usage against the usage requirements specified in the profile defining the computing resource cohort. Such compliance decisions typically indicate that the cohort has sufficient available resources to ensure an additional “buy” of the CEEC, and that the cohort resources are transferred to other computing resource cohorts. Cohort resources must be used at an efficient level to ensure that they are fully used to guarantee the “sale” of any CEEC, or that no changes should be made, ie “pending” actions must be performed This is done to determine whether usage of the computing resource cohort indicates that it is being used. Accordingly, a computing resource cohort can be designated as a buyer or seller in the CEEC market, and the commercial unit in the CEEC market is CEEC, that is, the computing resource cohort is the buyer or seller of the CEEC. The decision as to whether it is a buyer or seller may be made based on computing resource usage analysis and compliance with CEEC terms.

  Certain actions identified via the CEEC compliance action can be performed via the CEEC market maker service 322 using the transaction builder 312. That is, a transaction can be created to buy or sell the CEEC. CEEC Market Maker Service 322 processes transactions by pairing buyers and sellers according to the rules and parameters specified in CEEC Market Maker Service 322 to achieve the business goals of the organization or company. can do. These rules or parameters, along with evaluation criteria and CEEC compliance decisions, essentially define market factors for migrating CEEC through the CEEC cohort and associating with computing resources through the computing resource cohort To do. The actual operation of the CEEC market maker service 322 will be described in more detail below.

  FIG. 9 is a flow diagram outlining an example operation for assessing CEEC compliance in accordance with an illustrative embodiment. As shown in FIG. 9, this operation begins with the identification of CEEC conditions for the CEEC for which compliance is evaluated (step 910). Collect computing resource usage information from the computing resource cohort (s) with which the CEEC is associated (step 920). A determination is made as to whether the computing resource usage information indicates that the computing resource is being used in compliance with CEEC conditions (step 930). If so, a “pending” action is performed so as not to make any changes to the CEEC cohort or computing resource cohort (s) (step 940). If the computing resource usage information indicates that the CEEC conditions are not observed, the computing resource cohort (s) corresponding to the CEEC cohort becomes the CEEC seller in the CEEC market maker system As such, a “sale” action may be performed (step 950). If the computing resource usage information indicates that the computing resource cohort (s) have sufficient resources to accept additional CEECs (step 960), these computing resource cohorts ( Can be the buyer in the CEEC market maker system (step 970). Thereafter, this operation ends.

  Therefore, through the CEEC compliance operation, the computing resource provider not only provides the necessary resources based on the CEEC, but the contracting party uses the computing resources in compliance with the terms of the CEEC. Thus, it can be determined whether or not the CEEC condition is satisfied. If the CEEC terms are not satisfied by the contracting party, the CEEC is migrated to or denied from other computing resource cohort (s) and the same or different computing resource cohort (s) May be replaced with a new CEEC in the same or different CEEC cohort in relation to or may simply be eliminated without being replaced if desired. This effectively enforces the terms of the CEEC for computing resources and contracting parties.

  The negated CEECs are not all equal. For this reason, it is important that the company can prioritize the CEEC that can be priced to the highest value of the most costly CEEC or that provides the easiest savings.

  In addition, companies have the ability to deny CEEC on any other basis to meet their short-term or long-term goals through the CEEC market maker described below. Such a denial overrides any automated CEEC compliance assessment. Thus, an enterprise may, for example, deny a system's CEEC at that data center without having to terminate the lease at that particular data center and move all workloads to another location. it can.

CEEC Market Maker Service CEEC market making generally refers to convening CEEC buyers and sellers in an environment where it is difficult to find each other without assistance. The CEEC Market Maker Service 322 is a mechanism that convenes a Buyer Computing Resource Cohort and a Seller Computing Resource Cohort, either reallocating an existing CEEC from a seller to a buyer, or a seller To transfer the CEEC from the selling computing resource cohort to the buying computing resource cohort, either through denying the CEEC at and replacing the CEEC with a new CEEC associated with the buyer It facilitates transactions. In some cases, the CEEC market maker service goes beyond a simple match between the buyer and seller on a synchronous transaction that is known to both the buyer and seller of the CEEC at the time of the transaction. In addition, the CEEC market maker knows one side of the transaction (buy or sell) and uses the specified criteria to find the other (buy or sell) Or, it can provide the capability for asynchronous transactions associated with scheduled purchases that are not yet close to the buyer.

  Transactions for associating buyers and sellers within the CEEC Market Maker Service will create new CEECs that meet CEEC requirements or more closely match actual usage based on previous CEECs. As well as facilitating the transfer of the CEEC to be associated with an operating resource cohort, it functions to achieve business goals for an organization or company. That is, an organization or company can specify rules and parameters to guide how the CEEC market maker service 322 functions to achieve a particular goal. Examples of such goals include, for example, achieving a specific energy conservation goal, selling all of the CEEC associated with that location to yield a given location within the organization, purchasing new hardware, etc. To build a business case with a specified target dollar amount, to set the usage of the highest value asset (computing resource) to the target usage, a new computing resource (e.g. hardware or Software), identify the location for new computing resources that will result in the greatest cost savings, and prioritized list of CEEC migrations based on migration estimates Can be included.

  The basic goal of the CEEC Market Maker Service 322 is to achieve a desired goal with a migration source such as outdated hardware / software, underutilized hardware / software, etc. (sale Side) to a migration sink, eg new hardware / software, virtualization costs, etc. (buying side) and to the migration of the associated workload. CEEC market for potential buyers and sellers in response to the result of a CEEC compliance action or based on a desired goal and one or more transaction designations generated to achieve such desired goal By performing independent identification with manufacturer service 322, a cohort can be designated as a buyer or seller in this CEEC market. This migration is facilitated by transactions operated by the CEEC market maker service 322. Transactions can be generated automatically, semi-automatically, or manually via transaction builder 312.

  There are four phases in the life cycle of a transaction within the CEEC Market Maker Service 322: creation, assembly, initiation, and settlement. During the creation phase, transaction builder 312 is used to create transaction records from scratch or from existing transaction specifications. The creator of the transaction can be a human user or an automated process, specifying via the transaction builder 312 for buyers and sellers, eg, CEEC terms for sellers, buyer computing resources or cohorts Create or select configuration requirements, etc., for example, these selected designations will determine which operating system will run on the computing resource, what type of processor, processor quantity, memory resource Amount, CEEC business purpose, organizational unit associated with the CEEC, or any other identifiable parameter for the computing resource or CEEC may be included. This selection can be performed, for example, by allowing an authorized user to enter various specified parameters in the user interface element of transaction builder 312, selecting these specified parameters from a list of potential specified parameters, etc. .

  During the transaction structuring phase, the transaction creator or automated CEEC market maker service 322 uses this designation to specify a particular selling and buying computing resource cohort within an organizational or enterprise system. Select. For example, the seller may select from a set of computing resource cohorts that indicated a desire to become a seller as a result of a CEEC compliance action indicating the need to sell the CEEC. Instead, regardless of whether or not they have indicated a desire to be a seller, by selecting a computing resource cohort to be a seller, a seller can be selected according to a specified business goal.

  Similarly, a buyer can select from a set of computing resource cohorts that have indicated a desire to become a buyer. Such an indication can be made in response to a determination that the computing resource cohort is not fully utilized and is capable of accepting additional CEECs. Instead, a buyer can be selected according to a specified business goal by selecting a computing resource cohort to become the buyer, regardless of whether or not they have indicated a desire to be the buyer. In addition, a combination of these techniques can be used to select a buyer.

  The CEEC market is transitioned if the seller and buyer are identified by non-compliance with or both of the available computing resource capacity within the computing resource cohort and / or CEEC terms determined by CEEC compliance actions It serves as a way to migrate CEEC to a computing resource cohort that is likely to be used in a way that satisfies CEEC. As described above, this migration may result in the CEEC associated with a computing resource cohort (s) that may deny the original CEEC being migrated and provide computing resources based on the new CEEC. A new CEEC may be created in association with the cohort. This new CEEC may have different usage requirements than the original CEEC based on the actual measured usage of the CEEC by the contracting party. Thus, for example, based on the first CEEC, a contracting party was required to use computing resources at a first intensity pattern with an average usage of Y every week at a first level of X. Instead, if the measured usage shows a second intensity pattern with an average usage of W every week at a usage level of Z, the original CEEC may be negated and the average usage is W every week A new CEEC may be created that requires a usage status of Z in the intensity pattern. This new CEEC can be added as a member of a CEEC cohort associated with a computing resource cohort that can satisfy the conditions of the new CEEC. In such a situation, the original computing resource cohort associated with the original CEEC cohort is the seller of the original CEEC, and the computing resource cohort associated with the new CEEC cohort is the new CEEC within the CEEC market. The purchase side.

  Regardless of whether the cohort indicated a desire to become a buyer / seller, if a seller and buyer are selected based on business objectives, they can identify the business objectives and meet these business objectives A corresponding computing resource cohort with characteristics is selected. It analyzes computing resource usage information, configuration information, CEEC cohort association information, etc. to determine which computing resource cohort has characteristics that meet the criteria specified in the business goals. Can be included. As an example, if the business goal is to move the CEEC and its associated workload from a desktop computing device to a virtual machine or server, the computing resource cohort that includes the desktop computing device Based on configuration information associated with computing resources of the computing resource cohort. These computing resource cohorts can be selected as CEEC sellers within the CEEC market. Similarly, a computing resource cohort having configuration information indicative of a server computing device having a virtual server software mechanism can be identified and selected as a purchaser of CEEC within the CEEC market.

  During the assembly phase, both the selected seller and buyer will signal the acceptance or rejection of the transaction. The transaction can be settled at some point in the future, so that the selected sellers and buyers become “candidate” buyers and sellers that have not yet been confirmed. However, buyers and sellers may not be able to fulfill their obligations at the time of settlement, even though they are not negligent. The assembly phase will not be completed until there are enough sellers and buyers to meet the transaction goals. Thus, for example, if the business objective of a transaction is to migrate CEEC and its workload from all computing resources associated with an organization's site A, all computing resource cohorts associated with site A will Identified as a CEEC seller and sufficient computing resource cohorts at other sites are identified as buyers of CEEC to be able to migrate all CEEC cohorts associated with Site A to other sites Until the transaction assembly phase is completed.

  In response to determining that there are sufficient buyers and sellers to satisfy the business organization of the transaction, and determining that the outcome of the transaction is satisfactory, the transaction initiation phase is executed. The The determination of whether the transaction establishment result is satisfactory can be made automatically by comparing the establishment metric with one or more predetermined thresholds. For example, costs and values can be associated with various aspects of a transaction and these costs and values are calculated for a particular selected buyer and seller to determine the actual cost / value metric for that transaction. be able to. This cost / value metric for the transaction is then compared against one or more thresholds to provide sufficient benefits for the organization / enterprise that must complete the transaction. It can be judged whether value shows.

  Assuming that the cost / value metric indicates that it is desirable to complete the transaction, the necessary actions are initiated to facilitate the CEEC transition from the seller to the buyer. These operations may include, for example, performing various resource reservations using, for example, the CEEC-based resource reservation service 328 of FIG. This operation may also include operations to prepare for workload migration based on CEEC, including, for example, eliminating any non-essential storage, creating an OS instance container on the buyer system, etc. it can. Once these actions have been performed, the buyer and seller are no longer candidates and now formally have an obligation to fulfill the requirements of the transaction between the buyer and seller that together satisfy the transaction. . In order to ensure proper settlement of the transaction, a timer for any limited-time obligation based on the transaction can be started and monitored.

  When a transaction is initiated, an action is initiated to execute the actual transaction between the buyer and seller until the transaction is settled, i.e., all transactions are successfully completed. During this process, the transaction is said to be in the settlement phase. During this settlement phase, individual CEECs can be invalidated, created and modified based on the individual basis required to perform the various actions necessary to execute the transaction. For example, the CEEC can be invalidated in the CEEC cohort associated with the selling computing resource cohort, and a new CEEC can be generated and associated with the CEEC cohort associated with the buying computing resource cohort. As another example, the association of a CEEC and a CEEC cohort can be changed in the CEEC to point to a different CEEC cohort, i.e. the ID of the original CEEC cohort associated with the selling computing resource cohort is purchased. The CEEC is modified to be overridden with an ID that points to the CEEC cohort associated with the side computing resource cohort.

  How to determine the cost / value (cost / value) of a transaction before conducting a transaction based on a transaction specification and whether to actually execute the transaction and how to prioritize it against other ongoing transactions Can be used as a basis for judgment. The cost / value can be determined according to the cost attribute specified in the transaction specification. That is, transaction specification specifies the cost attributes that are of interest in determining whether the cost / value of a transaction is such that the transaction must be executed for both the buyer and seller. Can do. These costs and values need not be specified as monetary units, but can be any unit suitable for comparison between the buyer and seller. If the value of the transaction exceeds the cost by a threshold amount and the threshold is predetermined or can be specified in the transaction specification, the transaction should be allowed to be performed, otherwise the transaction Should be canceled.

  The actual cost calculation is based on the computing resources and support infrastructure used on the selling side to execute the workload based on the CEEC that is to be traded, and on the basis of the CEEC or replacement CEEC. Predictive computing resources that need to be used on the purchaser side to execute the workload can be executed based on computing resource usage metric information about the purchaser and the seller. The cost information on the selling side can be obtained from the database system 330, and the cost information on the purchase side includes the configuration information from the database system 330, the configuration information on the purchase side, and the measurement usage of the workload related to the CEEC. Based on configuration information from a prediction mechanism that predicts what costs will be incurred on the buying computing resource cohort. These operations may be performed within the logic of the CEEC market maker service 322, for example, in determining to initiate a trunk based on the transaction specification provided by the transaction builder 312.

  The specific costing framework used to evaluate a transaction can take a wide variety of forms, but in general, fixed asset costs, infrastructure costs, hardware resource allocation costs, and software instances • Costs will be taken into account. Fixed asset cost is the cost of providing a hardware platform to a software instance, regardless of how many or few software instances are treated as hosts. That is, the test to determine whether the cost must be encapsulated in this variable is the degree that it must be shared regardless of the software instance. The hardware resource allocation cost is a percentage of the hardware cost that must be charged for a given software instance based on its size (and therefore percentage hardware utilization). Software instance cost is the cost of a particular software instance. When migrating from many older systems to newer, higher density systems, there are easily calculable differences in the amount of floor space required, the amount of wattage consumed, and the amount of cooling required. . This difference is even greater when moving to an existing buyer.

  As mentioned above, when a transaction reaches the settlement phase, certain parties (buyers and sellers) that are associated with each other to execute transactions based on that transaction cannot fulfill its obligations, for example, It may be the case that resources that were available at transaction initialization are not available at that time. In such a case, the organization / enterprise can maintain a reserved computing resource as a whole, which can be organized into one or more computing resource cohorts and perform settlement to be completed Can be used for. However, these reserved computing resources need to be reacquired over time so that they can be used to assist in the settlement of future transactions if necessary. Accordingly, the CEEC market maker service 322 can generate a schedule for transferring CEEC and workload from these reserved computing resources to the purchasing computing resource cohort, A computing resource, or at least a portion thereof, provides a time frame indicating a desire to become a CEEC or a portion of a CEEC seller within the CEEC market. This schedule can be accelerated over time so that more frequent sales occur as time spent using reserved computing resources to facilitate CEEC.

  In addition, reservations for the CEEC market maker service 322 can be balanced to benefit the factors that the buyer may be aware of, such as storage. In such cases, the transaction divides its storage separately and credits the reserved resource, and the rest of the transaction proceeds as usual.

  Thus, the reserved computing resources of the CEEC-based computing resource management system 300 allow transaction parties to be complete even if the other party fails to fulfill their obligations. This confirms that the CEEC market maker service 322 does not function only as a neutral side to connect, but instead represents a “business intention” and achieves the business goals of the business. It is another case where it is important to recognize that the company has the resources to do so.

  10-13 are example diagrams illustrating various phases of a transaction for an example CEEC market maker-based scenario according to an illustrative embodiment. In the depicted example, from a secondary use desktop, i.e., a desktop that is not a primary computing resource for a contracting party, a virtual on one or more shared server computing devices that are virtualized clusters. A transaction designation 1014 for moving the CEEC is generated in a computer or the like. That is, transaction specification 1014 is used to move the CEEC and its associated workload from a physical computing device to a virtual computing device within a virtualized environment. The transaction specification 1014 may be pre-defined and already stored in connection with the transaction builder 1012 or user input 1010 received from a scratch and authorized user via a user interface provided by the transaction builder. Can be generated from If the transaction specification is predefined, the user input 1010 may be a predefined transaction specification from a transaction specification store associated with the transaction builder 1012, such as via a list in the user interface provided by the transaction builder 1012. There is a possibility to choose. If the number of sellers exceeds the ability to handle the transition at once, the existing transaction template allows an enterprise to easily recreate a transaction that must be executed many times.

  The selection or creation of transaction specification 1014 is performed using transaction builder 1012 during the transaction creation phase. In addition, transaction specification 1014 is provided to CEEC market maker service 1016, which generates one or more trading instances 1018 from the transaction specification. Trade instance 1018 is a transaction-specific instance used to execute trades between at least one seller and at least one buyer, where seller and buyer are computing resource cohorts . In the depicted example, transaction specification 1014 is used as a secondary workstation for CEEC for 50 virtual machines (VMs) on a particular virtualization cluster consisting of multiple computing resources. Used by the CEEC market maker service 1016 to create a trading instance 1018 for trading CEEC for a particular desktop. This trading instance 1018 is used to find a CEEC such as a CEEC corresponding to the CEEC designation 1022 for the seller and a CEEC such as a CEEC instance corresponding to the CEEC designation 1032 for the buyer that meets the criteria of the trading instance. Used by service 1016.

  Although only a single sell side CEEC designation 1022 and a single buy side CEEC designation 1032 are shown in FIGS. 10-13, in practice, multiple purchases generated from these CEEC designations 1022, 1032 are shown. It should be appreciated that the trading instance 1018 and transaction as a whole can be satisfied using the side and selling CEEC designations and / or CEEC instances. For example, in the depicted example, 50 seller CEEC instances corresponding to the CEEC designation 1022 can be used, one for each desktop, and the buyer buying all 50 seller CEEC instances. One buy-side CEEC instance corresponding to the CEEC designation 1032 can be used to represent the virtualized cluster.

  Following the creation of the transaction shown in FIG. 10, the transaction proceeds to the assembly phase shown in FIG. As shown, during the assembly phase, specific CEEC instances 1024 and 1034 associated with the sell side 1020 and purchase side 1030 of the transaction, respectively, are identified. This identification can be based on the CEEC market It can be executed by the manufacturer service 1016. Moreover, this identification may include an identification of the CEEC instance 1034 of the buyer 1030 that satisfies at least a portion of the purchaser requirements of the transaction specification 1014 and the transaction instance 1018. These CEEC instances 1024 and 1034 are associated with specific software resources 1025, 1035 and hardware resources 1026, 1036, which themselves have associated infrastructure and support (eg, human support) resources 1027, 1037. .

  In this phase of the transaction, the actual sell and buy cohorts are only considered “solicited” and are only candidate sell and buy parties. There is no requirement or expectation that the selling and buying cohorts will actually engage in the transaction until they inform the acceptance or refusal of trading instance 1018. If the sell / buy side accepts the transaction instance 1018 and determines that each portion of the transaction instance 1018 can be settled, it becomes confirmed, as shown in FIG. If the refusal of the transaction instance 1018 is notified, it is removed as a candidate for satisfying the transaction instance. Thus, in the transaction assembly phase, the sell and buy sides are assembled to satisfy one or more transaction instances 1018 of transaction 1014. A determination can be made as to whether there are enough sell and buy sides to satisfy the transaction requirements, if not, a sufficient number of sell and buy sides are identified and the transaction instance (s) 1018 The assembly phase can be continued until a time-out is accepted or if a timeout condition occurs. If a timeout condition occurs, an error condition can be returned indicating that the transaction could not be completed successfully. This error condition can be passed to the appropriate authorized user, such as via transaction builder 1012, so that appropriate corrective actions can be taken, such as changing the transaction specification, or manually selecting the sell and buy sides. You can be notified.

  If enough sell and buy sides are assembled during the transaction assembly phase, the transaction moves to the start phase as shown in FIG. During the start phase, the individual work required to execute the transaction instance (s) 1018 is started at 1060. The work that will be started (and will configure transaction payments when completed) includes, for example, configuring the virtualization infrastructure to provide 50 VMs, uninstalling unnecessary software Prepare the seller for migration by removing unnecessary files and making the image as small as possible, take a snapshot of the seller and re-establish it in the virtualization cluster Granting access to the appropriate user, removing all IP from the selling hardware, unplugging the selling hardware from the plug / rack, and removing the selling hardware from the floor Can be included. When the individual work is executed at 1060, the settlement phase of the transaction is executed as shown in FIG. During the settlement phase, the CEEC instance 1024 of the seller 1020 is traded to the buyer 1030 or otherwise discarded in exchange for a new CEEC 1034 on the buyer 1030, for example, each Fifty CEEC instances for a computer are traded or discarded and replaced with a single new CEEC instance representing 50 virtual machines on the virtualized cluster. As a result, as part of the settlement phase, the software resource license entitlement 1025 of the selling side 1020 associated with the traded / discarded CEEC instance (s) 1024 can be transferred to the buying side 1030. These software resource license entitlements 1025 can be associated with OS instances, i.e. assets or individuals, which will need to be correctly re-associated with the buyer cohort. The buyer is not expected to have “licensing capabilities”, but the market maker is presumed to have license liquidity if the seller needs additional licenses as it transitions (this is This may be the case if the software is licensed to an asset as “preload” that cannot be transferred) Typically, the hardware resource 1026 of the seller 1020 is no longer used by the contracting party of the CEEC instance 1024, and cessation of its resource (power, cooling, etc.) consumption is part of the transaction goal. There is a requirement that the hardware resource 1026 must be discarded. As software resources 1025 and hardware resources 1026 are migrated and discarded, the need for infrastructure and support resources 1027 is eliminated and the realized savings are associated with the transaction.

  As part of the settlement phase, a transaction result can be generated to evaluate the result of the transaction. Such transaction results can provide an indication regarding the costs associated with previous computing resource allocations as compared to new computing resource allocations after the transaction is completed. Such cost comparisons can be performed for a variety of costing factors, including power consumption costs, cooling costs, square feet costs, depreciation costs, support costs, license costs, and other costs / benefits. . In the depicted example, both of these costs simply reduced 50 desktop computers by reducing the number of hardware resources needed to support the entitlement of 50 contracting parties. In contrast to having it, it must be significantly lower for virtualized clusters. Such cost savings can be reported to an authorized user, such as via transaction builder 1012, for example, by providing a cost comparison via a user interface or the like.

  As a result of the transaction settlement phase, the transaction is completed and the completion can be notified to the transaction builder 1012 so that an appropriate notification can be generated and output to the authorized user. This notification can include a cost comparison result as described above. Next, the workload can be managed, routed and executed according to the new CEEC arrangement, the appropriate ticket can be generated to dispose of hardware resources, and support personnel and infrastructure resources can be reassigned You can do it. For example, a workload that would have been previously executed on 50 desktop computers is now executed using 50 virtual machine instances on the virtualization cluster, and each of the contracting parties has 50 Each one of the virtual machine instances is entitled to the CEEC 1034, and each contracting party is given the same degree of rights to the virtual machine instance associated with the CEEC instance 1034.

  FIGS. 14-16 depict example illustrations of transaction designations according to an illustrative embodiment. The transaction specification 1100 can be created by an authorized user via the transaction builder 312, for example. Transaction builder 312 constructs a transaction specification based on input from the user that specifies general characteristics of the company's goals and transaction type. However, when a transaction specification is created, any number of transaction instances that match the transaction specification can be automatically created by the transaction builder 312. The transaction designation is used to provide the CEEC market maker service 322 to identify buyers and sellers, determine the cost of the transaction, and whether to execute the transaction within the CEEC market based on these costs The transaction can be executed with the aid of other services, such as a CEEC-based reservation service 328, database system 330, etc.

  As shown in FIGS. 14-16, in the depicted example, there are five sections for a transaction. The first section 1110 of the transaction specification represents the savings goal, ie, what the transaction creator is trying to achieve in terms of savings. The second section 1120 represents a transaction sell-side filter, i.e., a filter that must be applied to potential sell-sides to select an appropriate sell-side to meet the business goals of the transaction. These sell-side filters may include architecture, geography, and business unit attributes, target values that indicate when a sufficient number of sell-sides have been selected, and the like.

  The third section 1130 of the transaction specification represents the selling side cost attribute. The sale side cost attribute is an attribute representing the cost of executing the workload on the sale side based on the current CEEC. These are not necessarily monetary costs, but can be specified, for example, by the amount of computing resource usage or reservation. As an example, a current CEEC-based workload may “require” 5 gigabytes of RAM, for example. As long as any cost metric can facilitate the cost / value comparison between the sell and buy sides of the CEEC market, such costs without departing from the spirit and scope of the exemplary embodiments. • Metrics can be used. The mechanisms of the exemplary embodiments have the ability to convert all costs into monetary units, but this is not essential for the exemplary embodiments to function.

  The fourth section 1140 of the transaction specification represents the buyer filter. This represents a filter that must be applied to a set of potential buyers in order to select an appropriate seller in order to meet the business goals of the transaction. These filters can include architectural, geographic, and business unit attributes, as well as target values that indicate when enough purchasers have been selected to meet the business goals of the transaction.

  The fifth section 1150 of the transaction specification represents the purchase side cost. The purchaser cost is the cost of executing the selling workload based on the existing or new CEEC on the buyer computing resource cohort (s). Again, these are not necessarily monetary costs, but can be specified for computing resource usage / reservation. These costs can be converted into monetary amounts, but this is not necessary for the operation of the exemplary embodiments.

  Transaction designation is a context for executing transactions between buyers and sellers in the CEEC market, so that CEEC can be transferred from seller to buyer to achieve the overall objective of the organization / enterprise. Provide guidance. Thus, transaction designation further represents the introduction of market factors by non-neutral CEEC market makers to direct the CEEC market to achieve business objectives. The actual transaction that is executed is made to CEEC as the item being traded. That is, the selling side is the selling side of the CEEC or part of the CEEC, and the buying side is the buying side of the CEEC or part of the CEEC.

  The CEEC market maker mechanism 322 of FIG. 3 provides CEEC from low-efficiency means to high-efficiency means, for example, from desktop to virtual server, from older technology laptop computers to newer server-based services. And the associated computing workload movement can be maximized and used to achieve organizational / enterprise optimization according to the predetermined objective of the organization / enterprise as specified in the transaction specification. That is, an organization may attempt to optimize the usage of its real estate, optimize its usage of power, optimize its usage of management resources in a particular geography, and so on.

  Same to ensure that a given CEEC does not need to represent the entire single system and its use by creating a contract document, ie, a CEEC, to allow trading of the CEEC and its associated workload A mechanism for decomposing and aggregating CEEC is also provided. Briefly, with the example embodiment CEEC and CEEC market maker service 322, entitlement and its workload do not need to be migrated from System A to System B as a whole, and therefore the migration is performed. There is no need to obtain agreement between the owners of System A and System B to do so. In contrast, the CEEC mechanisms of the exemplary embodiments divide System A's CEEC and its associated entitlement and the corresponding workload into the required number of fragments, making it easier than the entire contract (CEEC). You can find a deal that can sell a piece of. That is, when CEEC is the basis of trading instead of workload, each of these trading units (CEEC) is aggregated separately for the purpose of removing barriers to the movement of trading units (CEEC), An environment is created that can be disassembled, bound, and rebound. Workloads cannot be aggregated, disassembled, bound, and rebound without introducing the possibility of workload damage.

  Thus, for example, with the CEEC and CEEC Market Maker Service 322 of the exemplary embodiments, a single counterparty that wants to buy everything that the seller wants to sell or that the buyer wants to buy You can have more than two parties so that you can trade between a wide variety of parties, eliminating the need to find the seller you want to sell all. For example, the counterparty (buyer or seller) may only have one element of the CEEC, eg, the purchaser is the purchaser of the storage contract (ie, has available storage capacity), or only the storage terms or Since it has management support resource capacity but no hardware capacity, only the management support condition of CEEC can be cut out. Therefore, the CEEC can be disassembled into a plurality of components, so that the pools on the purchase side and the sale side are increased. Therefore, there is a high possibility that the CEEC market maker service 322 can pair the purchase side and the sales side of the CEEC. As a result, the business objective specified in the transaction designation is more likely to be achieved through the CEEC market maker service 322.

  Moreover, it should be recognized that “buyers” and “sellers” within the CEEC market are computing resource cohorts and not necessarily a single system. A buyer is a computing resource cohort that can obtain additional CEECs or portions of CEECs. The seller is a computing resource cohort that can yield CEEC or a portion of CEEC to the buyer. Thus, the essential function of CEEC market making is not primarily to convene a single seller and a single buyer, but this is possible through the mechanisms of the exemplary embodiments, For example, a single sale side of 1000 units and a single purchase side of 1000 units are possible. On the other hand, the CEEC market finds, for example, a buying group or cohort and a selling group or cohort that wants to sell 1000 units and want to buy 1000 units when combined. The important point is that in a market where both parties can have transactions between a pair of parties that are obligated to buy / sell exactly the same amount of each other, the number of transactions will be dramatically reduced, Applying this to system optimization reduces the opportunity to optimize the system. Thus, the mechanisms of the exemplary embodiments allow for the decomposition of a unit of trade, ie, CEEC, and can successfully complete a larger number of transactions through the CEEC market, and thus within the organization / enterprise Opportunities and possibilities to achieve optimized and more efficient allocation and use for computing resources can be increased.

  In the exemplary embodiments, the CEEC market maker service 322 appropriately defines transaction designations, uses a CEEC-based mechanism to determine buyers, sellers, and one or more buyers and ones. Pairing one or more sellers allows several different types of transactions in several different scenarios. For example, according to the mechanisms of the illustrative embodiments, one type of transaction may have a portion of the computing resources that one computing resource cohort is “near” and the other computing resource CEEC from one computing resource cohort that is underutilized or obsolete to two different computing resource cohorts based on the inclusion of computing resources to be acquired by an organization or company May be trading. In such a situation, the CEEC conditions to be sold can be divided between the two computing resource cohorts, for example, the CEEC storage capacity conditions are within the first computing resource cohort. It can be divided into available storage capacity and storage capacity made available on the hardware purchased in the second computing resource cohort.

  In another scenario, the CEEC for exclusive use of a computing resource can be disabled when the computing resource is not fully used within the selling computing resource cohort. This revoked CEEC can trade for a new CEEC where unused computing resources are shared with others based on the terms listed in the new CEEC contractually. In this example, the workload does not move, but rather shares its existing capacity and re-establishes a valid CEEC in this way.

  In yet other scenarios, the CEEC can be migrated from physical hardware computing resources to virtualized computing resources, eg, from a desktop to a virtual machine (VM). When migrating from a physical device to a virtual device, for example, from a desktop to a VM, user entitlement and workload is reduced from a mediocre high-capacity local storage paradigm to storage virtualization and higher price per gigabyte. To the storage paradigm. A large amount of cheap, unobstructed local storage in the physical device area correctly fills this local storage because users correctly assume that there will be no extra cost to fully use that local storage. Tend to. This presents a migration problem as each extra gigabyte moves to storage that is significantly more costly.

  Exemplary embodiments divide the storage component of the CEEC, make the storage component “time-bombing”, and the “supplied storage” is a fixed percentage each month after the CEEC transition. By reducing it, it provides a solution to this problem. Thus, for example, upon migration, the VM is allocated 200 gigabytes of storage with an explicit CEEC-based agreement that this allocation is reduced by 25% each month until the allocation reaches 50 gigabytes. Thus, the user is obligated to delete unnecessary software and data (duplicate data, redundant backups, back-level software, software that is no longer used, etc.) from the system in accordance with contractual obligations. We will conclude such a CEEC after knowing that the user can make such a reduction and that it takes some time to identify and delete 150 gigabytes of unnecessary data. It will be.

  FIG. 17 is a flowchart outlining an exemplary operation for performing a CEEC market transaction according to an exemplary embodiment. The operations outlined in FIG. 17 provide an interface by which, for example, primarily with the CEEC market maker service 322 of FIG. 3, a transaction specification can be generated and presented to the CEEC market maker service 322, for example. It can be executed using the transaction builder 312.

  As shown in FIG. 17, this operation can be used to receive user input or select an existing transaction specification that defines the transaction specification for use in executing one or more transactions of a transaction. Beginning with receipt (step 1210). Based on the transaction specification, an instance of the transaction specification is created (step 1220). The selling and buying computing resource cohorts are selected according to the criteria specified in the transaction specification (step 1230).

  Both the selected seller and buyer inform the acceptance or rejection of the transaction (step 1240). When notifying acceptance, the selected sellers and buyers are “candidate” buyers and sellers, and when rejecting, they are removed from the selected potential sellers and buyers. A determination is made as to whether there are enough sell and buy sides to meet the transaction goal (step 1250). If not, the operation returns to step 1230 to allow additional selections until sufficient sell and buy sides are selected and agreed to be candidates.

  In response to the existence of sufficient sell and buy sides to meet the transaction goals, the transaction start phase is executed (step 1260). A determination is made as to whether the cost / value of the transaction indicates that it is desirable to complete the transaction (step 1270). If not, the transaction is aborted (step 1280) and the operation ends with an error. If so, the various operations necessary to perform the CEEC transaction between the buyer and seller in order to achieve the business purpose of the transaction (step 1290). A determination is made as to whether any of the transactions for completing the transaction has not been successfully settled (step 1300). If successful, the booking computing resource can be used to ensure successful settlement of these transactions (step 1310). Thereafter, the completion of the transaction is notified to the CEEC market maker service (step 1320), and the operation ends.

  Accordingly, the CEEC market maker service of exemplary embodiments provides for a computing environment entitlement agreement (CEEC) in a data processing system that includes at least one computing device and a plurality of computing resources. A mechanism is provided for monitoring the use of computing resources. These mechanisms establish one or more CEEC data structures by at least one computing device, each CEEC data structure being a condition of a business level agreement between a contracting party and a data processing system provider. Can be used to define A CEEC condition specifies a set of computing resources with a specified configuration that will be used by a contracting party for a specified purpose at a specified level and pattern intensity for a specified period of time. . These mechanisms associate one or more CEEC data structures with a computing resource cohort by at least one computing device, and the computing resource cohort has a similar configuration of computing resources. It can operate further to become a set.

  In addition, these mechanisms identify the selling side of one CEEC data structure in one or more CEEC data structures by at least one computing device, and the selling side is designated in the CEEC data structure. It can operate to be a computing resource that is not being used according to conditions. Further, these mechanisms identify, by at least one computing device, a purchaser of one CEEC data structure in one or more CEEC data structures, with the purchaser specifying the conditions specified in the CEEC data structure To be a computing resource that can satisfy

  These mechanisms can also operate to determine whether to move the CEEC data structure from the selling side to the buying side based on the conditions specified in the CEEC data structure. This determination can be further made, for example, based on business goals and other criteria specified in the transaction designation. In response to the determination that the CEEC data structure should be migrated from the selling side to the buying side, the CEEC data structure can be migrated from the selling side to the buying side. This transition will invalidate the CEEC data structure associated with the seller, create a replacement CEEC data structure associated with the buyer, CEEC data that is no longer associated with the seller and is now associated with the buyer It may be necessary to change the structure, for example, update the ID of the cohort association.

  Thus, in the above mechanism, CEEC Market Maker Services selects buyers and sellers according to the terms of the transaction specified that define the terms of the CEEC to be migrated and the business objectives that must be met by such a migration of CEEC. To work. These mechanisms are either for the buyer to express interest in becoming a buyer in the CEEC market, for the seller to express interest in becoming a seller in the CEEC market, or to become a buyer or seller. It can operate in situations where a buy side and a sell side are selected regardless of the expression of interest. Moreover, these mechanisms may operate to pair a single buyer and multiple sellers, a single seller and multiple buyers, or multiple buyers and multiple sellers. it can. To facilitate such one-to-many, many-to-one, or many-to-many transactions, the CEEC can be broken down, the buyer can buy individual parts of one CEEC, and the seller can Each part of the CEEC can be sold to a different buyer.

  In other implementations, according to exemplary embodiments, the computing resource cohort registers with the CEEC Market Maker Service as a Buyer and Seller, and the Seller / Purchase enabled by the CEEC Market Maker Service. You can specify the criteria by which the side can be selected. Such criteria can be used either exclusively or in combination with, for example, conditions specified in the transaction specification. In that case, the CEEC Market Maker Service will pair one or more buyers with one or more sellers according to the conditions specified when registering as a buyer / seller and the conditions specified by the transaction. To work. For example, in response to the CEEC compliance action indicating that the computing resource of the computing resource cohort associated with the CEEC has not been used by the contracting party in accordance with the terms of the CEEC, a registration notice is sent to the CEEC market maker. Send to service 322 to add a computing resource cohort to the CEEC market as a CEEC seller and specify conditions for buyer selection; for example, Sufficient resources and resource types must be provided to satisfy the actual usage measured for the parties, and a new CEEC consistent with such actual usage measurements must be provided.

  That is, the illustrative embodiments describe the use of computing resources for a computing environment entitlement agreement (CEEC) in a data processing system that includes at least one computing device and a plurality of computing resources. A mechanism for monitoring can be provided. These mechanisms establish one or more CEEC data structures, each of which defines the terms of a business level agreement between the contracting party and the data processing system provider. A CEEC condition specifies a set of computing resources with a specified configuration that will be used by a contracting party for a specified purpose at a specified level and pattern intensity for a specified period of time. . These mechanisms receive a first request from a provider system that provides one or more computing resources to request to be a purchaser of a CEEC data structure, and the first request sells the CEEC data structure. It further operates to include a first selection criterion for selecting a side.

  In addition, the mechanisms of the illustrative embodiments may operate to receive a second request from the selling system that owns the CEEC data structure requesting to become the selling side of the CEEC data structure. The second request may include a second selection criteria for selecting a purchaser of the CEEC data structure. In that case, the CEEC market maker service can pair the provider system with the selling system based on the first and second selection criteria and migrate the CEEC data structure from the selling system to the provider system. can do. The workload can then be executed on the provider system according to the conditions specified in the CEEC data structure. It should be appreciated that this migration may require invalidation of the original CEEC data structure and replacement with a new one having similar or different conditions. Moreover, instead of having to migrate the entire CEEC, the CEEC data structure can also represent only a portion of the CEEC.

  Accordingly, the mechanisms of the illustrative embodiments provide a contract mechanism that defines the required level and pattern of use by a contract party for a specified business purpose, i.e., a contracting party's entitlement to computing resources in CEEC. , Provides a mechanism for managing these entitlements and the consequences of the violation are the invalidation of the CEEC and a potential replacement by a new CEEC for the same or different computing resources. The new CEEC can be adjusted to accommodate the user's actual measurement usage, rather than the level and pattern usage required by the original CEEC.

  The illustrative embodiments further provide a mechanism for migrating such CEEC using the CEEC market, and market factors are introduced that represent the business goals of the organization or company. Because the workload is routed and the contracting party is associated with the computing resource cohort (s) associated with the new or migrated CEEC at this time, the result of this migration is related to the migrated CEEC. The workload is transferred. Again, however, it should be noted that the mechanisms of the illustrative embodiments are significantly different from known workload migration mechanisms.

  For example, a known workload migration mechanism is a one-to-one migration mechanism that means that one system migrates the entire workload to one other system. CEEC migration using the mechanisms of the illustrative embodiments splits a CEEC into multiple components so that each portion can be migrated to a different target computing resource or computing resource cohort Make it possible to do. In addition, the workload migration mechanism is indifferent and objective about business objectives and usage, i.e., it does not determine whether computing resources are being used for a specified business objective, and workload migration is efficient. Only determine whether it will increase Workload migration does not impose any obligation on migrating computing resources, especially to do something with older systems, and in many cases with the mechanisms of the illustrative embodiments, CEEC It includes a specific obligation to retire the older system, invalidates the CEEC if not performed, and therefore revokes the user's right to use the system in its new home.

  As previously mentioned, the mechanisms of the exemplary embodiments are different from other types of workload-based arrangements such as service quality assurance schemes, service quality agreements, and the like. In such an agreement, as long as the service level or quality of service that the provider is obligated to provide is paid by the contracting party, the provider will know how or how much of the service is used. I am not interested in The CEEC-based mechanism of the exemplary embodiments allows the CEEC to use the associated computing resources according to the contracted party's use of the levels and patterns specified in the CEEC according to the specified business objectives. Guaranteed, otherwise CEEC invalidation and / or migration is performed.

  In addition, users of SLA or QoS agreements are not worried about how services are provided, i.e., what specific types of computing resources are used to provide service levels or quality of service. . However, in the CEEC-based mechanisms of the exemplary embodiments, the CEEC can specify requirements for the type of computing resources that it intends to provide based on the CEEC, and the CEEC market maker can Enforce such conditions on behalf of companies to ensure that resources are provided in the most efficient and possible manner.

  As noted above, it is recognized that the exemplary embodiments can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment containing both hardware and software elements. I want. In one example embodiment, the mechanisms of the illustrative embodiments are implemented in software or program code, including but not limited to firmware, resident software, microcode, etc.

  A data processing system suitable for storing and / or executing program code will include at least one processor coupled directly or indirectly to memory elements through a system bus. Memory elements include local memory used during the actual execution of program code, mass storage, and at least to reduce the number of times code must be fetched from mass storage during execution. And cache memory that provides temporary storage for some program code.

  Input / output or I / O devices (including but not limited to keyboards, displays, pointing devices, etc.) can be coupled to the system either directly or through intervening input / output controllers. Network adapters may also be coupled to the system so that the data processing system can be coupled to other data processing systems or remote printers or storage devices via private or public networks. it can. Modems, cable modems, and Ethernet cards are just some of the currently available types of network adapters.

  The description of the present invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. This embodiment is intended to best illustrate the principles of the invention, practical applications, and to illustrate the invention with respect to various embodiments including various modifications suitable for the particular application contemplated by those skilled in the art. It has been chosen and described for the sake of understanding.

Claims (79)

A method for use in a data processing system including at least one computing device and a plurality of computing resources, the method for reserving computing resources of the data processing system, comprising:
Generating one or more computing environment entitlement agreement (CEEC) data structures by the at least one computing device, each CEEC data structure being a contract party and computing of the data processing system; Define the terms of a business level agreement with a provider of resources, said terms of said CEEC being at a specified level and pattern intensity for a specified period, for a specified purpose, for said specified party Specifying a set of computing resources with a specified configuration that is to be used by
Associating, by the at least one computing device, a set of one or more computing resources with a CEEC data structure;
Managing the set of one or more computing resources according to the associated CEEC data structure by the at least one computing device, the approximate specified in relation to the CEEC data structure; Responding to the contracting party's failure to use the set of one or more computing resources for the specified purpose, approximately at the specified level and pattern strength during the specified period The logic of the at least one computing device includes invalidating the CEEC data structure. Associating the set of one or more computing resources with a CEEC data structure;
Determining a first set of one or more computing resource cohorts comprising a plurality of computing resources based on configuration information about each of the computing resources; The plurality of computing resources associated with the resource cohort are of the same type and have the same configuration as each other;
Determining a second set of one or more CEEC cohorts of CEEC data structures based on attributes of the one or more CEEC data structures maintained by the data processing system;
Based on the conditions of the CEEC data structure associated with the selected CEEC cohort associated with the second set and the characteristics of the selected computing resource cohort associated with the first set, the selected CEEC The method of claim 1, comprising matching a cohort with the selected computing resource cohort.   The method of claim 2, wherein the CEEC data structure is associated with two or more CEEC cohorts based on individual conditions of the CEEC data structure.   Managing the set of one or more computing resources in accordance with the associated CEEC data structure may include the one or more computings in accordance with conditions specified in relation to the associated CEEC data structure. 4. A method according to claim 2 or claim 3, comprising routing the workload to a computing resource associated with the set of resources. Routing the workload is
Receiving a workload from the contracting party;
Retrieving one or more CEEC data structures associated with the contracting party;
Determining the type of the workload;
Determining the type of business purpose associated with the retrieved one or more CEEC data structures;
Routing the workload to a computing resource designated in at least one of the retrieved one or more CEEC data structures based on the workload type and the determined type of business purpose. The method of claim 4, comprising:   Routing the workload to a computing resource designated in at least one of the retrieved one or more CEEC data structures based on the workload type and the determined type of business purpose. 6. The method of claim 5, wherein finding a computing resource associated with a computing resource cohort associated with a CEEC data structure having a type of business purpose that matches the type of workload. Managing the set of one or more computing resources according to the associated CEEC data structure;
Collecting resource usage metric information from the one or more computing resources;
Comparing the resource usage metric information with usage intensity of the specified level and pattern specified in relation to the associated CEEC data structure;
Determining whether the collected resource usage metric information is significantly different from usage strength of the specified level and pattern specified in relation to the associated CEEC data structure;
Whether to change at least one of the membership of the CEEC data structure associated with a CEEC cohort in response to the collected resource usage metric information being significantly different from the strength of the specified level and pattern Or changing the association of the CEEC data structure to the set of one or more computing resources.   Changing at least one of the membership of the CEEC data structure or changing the association of the CEEC data structure to the set of one or more computing resources is at the specified level and pattern 8. The method of claim 7, comprising changing membership of the CEEC data structure by migrating the CEEC data structure whose usage intensity is significantly different from the collected resource usage metric information to a different CEEC cohort. .   Changing at least one of the membership of the CEEC data structure and changing the association of the CEEC data structure to the set of one or more computing resources includes changing the one or more computings. The set of one or more computing resources by migrating the CEEC data structure from being associated with a set of resources to being associated with a different set of one or more computing resources; The method of claim 7, comprising changing the association of the CEEC data structure to   Migrating the CEEC to a different CEEC cohort invalidates the CEEC whose usage level of the specified level and pattern is significantly different from the collected resource usage metric information; and The method of claim 9, comprising replacing with a new CEEC associated with a different CEEC cohort.   Migrating the CEEC to be associated with a different set of one or more computing resources from invalidating the CEEC from being associated with the set of one or more computing resources 11. The method of claim 9 or claim 10, comprising: creating a new CEEC; and associating the new CEEC with the different set of one or more computing resources. The CEEC data structure is
A CEEC ID field for designating identification information of the CEEC;
A contract party ID field that stores a contract party ID to clearly identify the contract party or group of contract parties with which the CEEC data structure is associated;
One or more clearly identifying the computing resource (s), computing resource cohort, or other group of computing resource (s) with which the CEEC data structure is associated A computing resource ID field;
Identifies one or more specified business purposes that can use the computing resource (s) identified in association with the one or more computing resource ID fields according to the CEEC One or more business purpose fields;
One or more computing resource configuration fields that specify computing resource configuration requirements to satisfy the conditions of the CEEC;
Specifies the usage intensity of a given level and pattern for a given period of time required by the contracting party to use the computing resource specified in one or more computing resource ID fields 12. A method according to any preceding claim, comprising one or more usage fields. An apparatus for reserving computing resources of a data processing system, comprising:
At least one processor;
At least one memory coupleable to the at least one processor when executed by the at least one processor;
Generating one or more computing environment entitlement agreement (CEEC) data structures, each CEEC data structure being a business resource between a contracting party and a computing resource provider of the data processing system. A defined configuration that defines the terms of a level agreement and that the terms of the CEEC are to be used by the contracting parties for a specified purpose at a specified level and pattern intensity for a specified period of time Specifying a set of computing resources having
Associating a set of one or more computing resources with a CEEC data structure;
Managing the set of one or more computing resources according to the associated CEEC data structure, wherein the designation is approximately during the designated period of time specified in relation to the CEEC data structure; In response to failure of the contracting party to use the set of one or more computing resources for the specified purpose at a level and pattern strength of the CEEC data structure And at least one memory comprising instructions that cause the at least one processor to be operable to invalidate. A method for use in a data processing system comprising at least one computing device and a plurality of computing resources, for monitoring the use of said computing resources against a computing environment entitlement agreement A method,
Generating one or more computing environment entitlement agreement (CEEC) data structures by the at least one computing device, each CEEC data structure comprising a contract party and a provider of the data processing system; The terms of the CEEC data structure are used by the contracting party for a specified purpose at a specified level and pattern strength for a specified period of time. Specifying a set of computing resources with a specified configuration to be
Generating, by the at least one computing device, a CEEC cohort including a set of CEEC data structures having similar conditions;
Monitoring, by the at least one computing device, usage of a set of computing resources according to the similar conditions of the set of CEEC data structures to identify usage patterns associated with the CEEC cohort;
Changing the membership of a CEEC data structure associated with the CEEC cohort based on the identified usage pattern by the at least one computing device. Generating a CEEC cohort containing a set of CEEC data structures with similar conditions;
Specifying the parameters used to define the CEEC cohort and defining a profile that specifies the characteristics of the CEEC data structure that can be included as a member of the CEEC cohort;
Applying the profile to data in a CEEC data structure of the one or more CEEC data structures to determine which CEEC data structure meets the requirements specified by the parameters of the profile;
15. The method of claim 14, comprising selecting one or more CEEC data structures for inclusion in the set of CEEC data structures of the CEEC cohort.   Evaluation to determine usage patterns associated with computing resource usage metric information required for membership of the CEEC data structure associated with the CEEC cohort as well as membership of the CEEC data structure associated with the CEEC cohort The method of claim 15, wherein the profile specifies at least one of computing resource usage metric information associated with the CEEC data structure.   17. A method according to claim 15 or claim 16, wherein the profile specifies the similar conditions that each CEEC data structure must have in order to become a member of the CEEC cohort. Items related to the CEEC database based on the selection of the one or more CEEC data structures to include an ID of the CEEC cohort in an item of the CEEC database corresponding to the selected one or more CEEC data structures 18. A method according to any of claims 15 to 17, further comprising updating.   The profile includes a first criterion for evaluating the computing resource usage metric information to determine whether a CEEC data structure is a member of the CEEC cohort, the first criterion comprising: 19. A method according to any of claims 16 to 18, comprising a time frame for performing the evaluation, a basis for measuring the computing resource usage, and a bias for performing the evaluation.   The profile further comprises a second criterion for determining when to adjust membership of a CEEC data structure associated with the CEEC cohort and a corresponding action associated with the second criterion. 19. The method according to 19.   The second criteria and corresponding action thus specify a range of computing resource usage metric information on which the corresponding action is performed, and the corresponding action uses the CEEC market to add the CEEC cohort A buy action to obtain an additional CEEC data structure associated with, a sell action to remove the CEEC data structure from the CEEC cohort using the CEEC market, a pending action to do nothing; 21. A method according to claim 20, comprising a discard action for permanently discarding the CEEC data structure. Changing the membership of the CEEC data structure associated with the CEEC cohort based on the identified usage pattern;
Applying the second criteria to the identified usage pattern to identify a corresponding action;
The method of claim 21, comprising performing the corresponding action on the CEEC data structure, thereby changing the membership of the CEEC cohort.   23. The method of claim 22, wherein the corresponding action performed on the CEEC data structure is to migrate the CEEC data structure to the different CEEC cohort according to other profiles associated with different CEEC cohorts.   Migrating the CEEC data structure invalidates the CEEC data structure associated with the CEEC cohort and creates a new CEEC data structure associated with the different CEEC cohort to replace the invalidated CEEC data structure. 24. The method of claim 23, comprising creating.   25. The method of claim 24, wherein the new CEEC data structure includes a different condition than the invalidated CEEC data structure, and the condition of the new CEEC data structure approaches the identified usage pattern. A device for monitoring the use of computing resources against a computing environment entitlement agreement, comprising:
At least one processor;
At least one memory coupleable to the at least one processor when executed by the at least one processor;
Generating one or more computing environment entitlement agreement (CEEC) data structures, each CEEC data structure defining the terms of a business level agreement between a contracting party and a data processing system provider And the condition of the CEEC data structure has a specified configuration to be used by the contracting party for a specified purpose at a specified level and pattern intensity for a specified period of time. Specifying a set of resources;
Generating a CEEC cohort that includes a set of CEEC data structures with similar conditions;
Monitoring the usage of a set of computing resources according to the same conditions of the set of CEEC data structures to identify usage patterns associated with the CEEC cohort;
And at least one memory comprising instructions that cause the at least one processor to change a membership of a CEEC data structure associated with the CEEC cohort based on the identified usage pattern. A method for use in a data processing system including at least one computing device and a plurality of computing resources for managing the use of said computing resources against a computing environment entitlement agreement A method,
Generating one or more computing environment entitlement agreement (CEEC) data structures by the at least one computing device, each CEEC data structure comprising a contract party and a provider of the data processing system; Define the terms of the business level agreement between the CEEC and the terms of the CEEC will be used by the contracting parties for the specified purpose at the specified level and pattern intensity for the specified period Specifying a set of computing resources having a specified configuration;
Generating, by the at least one computing device, a CEEC cohort including a set of CEEC data structures having similar conditions;
Collecting, by the at least one computing device, resource usage metrics that measure usage of respective computing resources associated with a set of computing resources according to the similar conditions of the CEEC cohort;
Calculating a relative measure of resource usage based on a respective CEEC data structure associated with the CEEC cohort based on the collected resource usage metrics by the at least one computing device;
Outputting, by the at least one computing device, the relative measure of the resource usage of each CEEC data structure associated with a CEEC cohort.   Computing a relative measure of resource usage based on a respective CEEC data structure associated with the CEEC cohort based on the collected resource usage metrics is based on a business goal associated with the CEEC cohort. 28. The method of claim 27, further comprising determining a relative weighting of resource usage metrics for various of the computing resources associated with a collection of resources.   30. The method of claim 28, wherein the relative weighting of resource usage metrics is dynamically determined based on a statistical analysis of the collected resource usage metrics. Calculating a relative measure of resource usage based on a respective CEEC data structure associated with the CEEC cohort based on the collected resource usage metrics;
Retrieving a profile corresponding to the CEEC cohort, which profile should use which computing resource usage metric as a basis for calculating a relative measure of resource usage for each of the CEEC data structures And specifying criteria for evaluating the computing resource usage metrics to calculate the relative measure of resource usage for each of the CEEC data structures;
30. A method as claimed in any of claims 27 to 29, comprising calculating the relative measure of resource usage based on the specified computing resource usage metrics and metrics associated with the profile.   The relative measure of resource usage includes a score, and outputting the relative measure of the resource usage of each CEEC data structure associated with the CEEC cohort is based on a condition of the corresponding CEEC data structure 28. Classifying the scores of each CEEC data structure from a plurality of categories of resource usage scores indicating relative usage of the computing resources into one category of resource usage scores. 30. The method according to any one of 30.   Each of the CEEC data associated with the CEEC cohort, wherein the relative measure of resource usage includes a score for the CEEC cohort as a whole based on a score for each of the individual CEEC data structures that are part of the CEEC cohort Outputting a report of the relative measure of the resource usage of a structure indicates a resource usage indicating a relative usage of the computing resource based on conditions of a CEEC data structure associated with the CEEC cohorts that are similar to each other 32. A method according to any of claims 27 to 31, comprising outputting one category of resource usage for the CEEC cohort as a whole from a plurality of categories of status scores. The method further comprises: updating a profile used to generate the CEEC cohort to include at least one of the score for the CEEC cohort and the category of resource usage for the CEEC cohort. 33. The method according to 32.   The category of resource usage scores includes a range of computing resource usage scores indicating whether the computing resource usage is within an acceptable tolerance of a condition of a corresponding CEEC data structure. 34. The method according to any one of 31 to 33.   Each of the categories of resource usage is to be performed on the corresponding CEEC data structure in response to the score associated with the CEEC data structure being categorized into the specific category of resource usage 35. A method according to any of claims 31 to 34, wherein the method has an action to:   The corresponding action includes at least one of denying the CEEC data structure, replacing the CEEC data structure with a new CEEC data structure, and migrating the CEEC data structure from the CEEC cohort to another CEEC cohort. 36. The method of claim 35. Changing membership of the at least one CEEC data structure based on a classification of scores associated with at least one CEEC data structure associated with a CEEC cohort;
37. A method according to any of claims 31 to 36, further comprising controlling workload execution based on the CEEC cohort and an association of the CEEC cohort with a set of one or more computing resources.   38. A method according to any of claims 31 to 37, wherein the category of resource usage score is determined based on a selection of at least one of an exemplary CEEC data structure and a reverse exemplary CEEC data structure in the CEEC cohort. . An apparatus for managing the use of computing resources against a computing environment entitlement agreement, comprising:
At least one processor;
At least one memory coupleable to the at least one processor when executed by the at least one processor;
Generating one or more computing environment entitlement agreement (CEEC) data structures, each CEEC data structure defining the terms of a business level agreement between a contracting party and a data processing system provider A computing resource having a specified configuration that the conditions of the CEEC are to be used by the contracting party for a specified purpose at a specified level and pattern intensity for a specified period of time Specifying a set of
Generating a CEEC cohort that includes a set of CEEC data structures with similar conditions;
Collecting resource usage metrics that measure usage of respective computing resources associated with a set of computing resources according to the same conditions of the CEEC cohort;
Calculating a relative measure of resource usage based on a respective CEEC data structure associated with the CEEC cohort based on the collected resource usage metrics;
And at least one memory including instructions that cause the at least one processor to output a report of the relative measure of the resource usage of each CEEC data structure associated with a CEEC cohort. A method for use in a data processing system that includes at least one computing device and a plurality of computing resources, transferring a computing environment entitlement agreement from one computing resource to another computing resource A method for
Generating one or more computing environment entitlement agreement (CEEC) data structures by the at least one computing device, each CEEC data structure comprising a contract party and a provider of the data processing system; Define the terms of the business level agreement between the CEEC and the terms of the CEEC will be used by the contracting parties for the specified purpose at the specified level and pattern intensity for the specified period Specifying a set of computing resources having a specified configuration;
Associating said one or more CEEC data structures with a computing resource cohort by said at least one computing device, said computing resource cohort having a similar configuration; Including a collection of resources;
Identifying, by the at least one computing device, a sell side of a CEEC data structure associated with the one or more CEEC data structures;
Identifying, by the at least one computing device, a purchaser of a CEEC data structure associated with the one or more CEEC data structures;
Transferring the CEEC data structure from the selling side to the buying side;
Executing a workload according to conditions specified in relation to the CEEC data structure at the purchase side after the CEEC data structure has been migrated from the sale side to the purchase side, wherein the sale side and the purchase side are A method comprising: including a computing resource or a collection of computing resources.   A computing system in which the selling party includes computing resources that are not used in accordance with the conditions specified in relation to the CEEC data structure, and wherein the buyer can satisfy the conditions specified in relation to the CEEC data structure; 41. The method of claim 40, comprising a resource.   42. The determination of whether to migrate the CEEC data structure from the selling side to the buying side is performed based on the conditions specified in relation to the CEEC data structure. The method described.   Determining whether to transfer the CEEC data structure from the selling side to the buying side based on a business target standard associated with transaction designation, wherein the CEEC data structure from the selling side to the buying side 43. A method according to any of claims 40 to 42, wherein the migration further comprises performing only in response to a determination that the migration of the CEEC data structure satisfies the business goal criteria associated with the transaction designation. the method of.   44. The transaction specification is generated by a user using a transaction builder user interface, and the user specifies the business goal criteria using the transaction builder user interface. Method.   Multiple transaction instances for migrating the CEEC data structure between the seller and buyer are generated based on the transaction specification, and CEEC data between the seller and buyer associated with each transaction instance. 45. A method according to claim 43 or claim 44, wherein the business goal criteria of the transaction specification are used with the transaction instance to determine whether to perform a structure migration.   Identifying the selling side of the CEEC data structure; identifying the buying side of the CEEC data structure; and transferring the CEEC data structure from the selling side to the buying side; 46. A method according to any of claims 43 to 45, wherein the method is performed according to a goal criterion.   The at least one of the seller or buyer does not indicate a desire to become a seller or buyer before identification of the seller and buyer in accordance with the business goal criteria of the instance specified in the transaction. 46. The method according to 46.   Migrating the CEEC data structure from the selling side to the buying side is performed according to the instance of the transaction specification, the creation phase of creating the instance of the transaction specification of the instance of the transaction specification; An assembling phase in which the selling side and the buying side inform acceptance of the transaction, a start phase in which a determination is made as to whether the completion of the transition meets the business target criteria associated with the instance of the transaction designation, and the transition 48. The method of claim 46 or claim 47, wherein the method is performed using four transaction phases including a settlement phase that is completed between the seller and the buyer.   The migrating the CEEC data structure from the selling side to the buying side includes invalidating the CEEC data structure on the selling side and creating a new CEEC data structure on the buying side. 49. A method according to any of 40 to 48.   50. Any of the claims 40-49, wherein transitioning the CEEC data structure from the sell side to the purchase side includes resetting an ID associated with the CEEC data structure to identify the purchase side. The method described.   The CEEC data structure represents a sub-portion of CEEC between the contracting party and the provider, and the portion of the CEEC that can be satisfied by the computing resources provided by the provider. 51. A method according to any of claims 40 to 50, wherein the method defines a portion of the condition and the other portion of the condition of the CEEC is satisfied by other computing resources provided by other providers. An apparatus for transferring a computing environment entitlement agreement from one computing resource to another computing resource, comprising:
At least one processor;
At least one memory coupleable to the at least one processor when executed by the at least one processor;
Generating one or more computing environment entitlement agreement (CEEC) data structures, each CEEC data structure defining the terms of a business level agreement between a contracting party and a data processing system provider A computing resource having a specified configuration that the conditions of the CEEC are to be used by the contracting party for a specified purpose at a specified level and pattern intensity for a specified period of time Specifying a set of
Associating the one or more CEEC data structures with a computing resource cohort, the computing resource cohort comprising a collection of computing resources having a similar configuration;
Identifying a sell side of a CEEC data structure associated with the one or more CEEC data structures;
Identifying a purchaser of a CEEC data structure associated with the one or more CEEC data structures;
Transferring the CEEC data structure from the selling side to the buying side;
Executing a workload according to conditions specified in relation to the CEEC data structure at the purchase side after the CEEC data structure has been migrated from the sale side to the purchase side, wherein the sale side and the purchase side are An apparatus comprising: at least one memory including instructions that cause the at least one processor to execute to include a computing resource or a collection of computing resources. A method for use in a data processing system that includes at least one computing device and a plurality of computing resources, transferring a computing environment entitlement agreement from one computing resource to another computing resource A method for
Generating one or more computing environment entitlement agreement (CEEC) data structures by the at least one computing device, each CEEC data structure comprising a contract party and a provider of the data processing system; Define the terms of the business level agreement between the CEEC and the terms of the CEEC will be used by the contracting parties for the specified purpose at the specified level and pattern intensity for the specified period Specifying a set of computing resources having a specified configuration;
Receiving, by the at least one computing device, a first request from a provider system that provides one or more computing resources to become a purchaser of a CEEC data structure, comprising: A first request includes a first selection criterion for selecting a sell side of the CEEC data structure;
Receiving a second request from the selling system that owns the CEEC data structure by the at least one computing device requesting to become the selling side of the CEEC data structure, wherein the second request is Including a second selection criterion for selecting a purchase side of the CEEC data structure;
Pairing the provider system and the selling system with the at least one computing device based on the first and second selection criteria;
Migrating the CEEC data structure from the selling system to the provider system;
Executing a workload on the provider system according to the condition associated with the migrated CEEC data structure.   The first selection criteria for providing a specific amount of computing resources and a specific type of computing resources to satisfy a measured computing resource usage corresponding to the CEEC data structure; 54. The method of claim 53, wherein at least one of the purchaser requirements is specified.   54. The claim 53 or claim wherein the second selection criteria specifies at least one of the seller's requirement to use a specific amount of computing resources and a specific type of computing resources. 55. The method according to item 54.   Determining whether to migrate the CEEC data structure from the selling side system to the buying side system based on a business goal standard associated with transaction designation, wherein the selling side system to the buying side system 56. The method of any one of claims 53 through 55, wherein the migration of a CEEC data structure is further performed only in response to a determination that the migration of the CEEC data structure satisfies the business goal criteria associated with the transaction designation. The method according to any one.   57. The transaction specification is generated by a user using a transaction builder user interface, and the user specifies the business goal criteria using the transaction builder user interface. Method.   A plurality of transaction instances for migrating the CEEC data structure between the selling system and the buying system are generated based on the transaction specification, and each of the selling and buying systems associated with each transaction instance. 58. The method of claim 56 or 57, wherein the business-specific criteria for the transaction specification are used with the transaction instance to determine whether a migration of CEEC data structures between them should be performed.   57. The pairing of the selling system and the purchasing system is performed according to the first selection criteria, the second selection criteria, and the business goal criteria of the transaction specified instance. 58. The method according to any of 58.   At least one of the purchasing systems separates the CEEC data structure into component parts based on the conditions associated with the CEEC data structure, whereby a plurality of purchasing system systems each purchase a portion of the CEEC data structure. 60. The method of claim 59, wherein the method is one of them.   Migrating the CEEC data structure from the selling side to the buying side is performed according to the instance of the transaction specification, the creation phase of creating the instance of the transaction specification of the instance of the transaction specification; An assembly phase in which the selling system and the purchasing system notify acceptance of the transaction, and a start phase in which a determination is made as to whether the completion of the transition meets the business goal criteria associated with the instance of the transaction designation; 61. The method of claim 59 or claim 60, wherein the transition is performed using four transaction phases including a settlement phase that is completed between the selling system and the buying system.   Migrating the CEEC data structure from the selling side to the buying side invalidates the CEEC data structure on the selling side system and creates a new CEEC data structure on the buying side system. 62. A method according to any of claims 53 to 61, comprising:   63. The method of claim 53-62, wherein migrating the CEEC data structure from the selling system to the buying system includes resetting an ID associated with the CEEC data structure to identify the buying system. The method according to any one.   64. A method according to any of claims 53 to 63, wherein each of the buyer system and the seller system includes a computing resource cohort that includes a plurality of computing resources. An apparatus for transferring a computing environment entitlement agreement from one computing resource to another computing resource, comprising:
At least one processor;
At least one memory coupleable to the at least one processor when executed by the at least one processor;
Generating one or more computing environment entitlement agreement (CEEC) data structures, each CEEC data structure defining the terms of a business level agreement between a contracting party and a data processing system provider A computing resource having a specified configuration that the conditions of the CEEC are to be used by the contracting party for a specified purpose at a specified level and pattern intensity for a specified period of time Specifying a set of
Receiving a first request from a provider system that provides one or more computing resources requesting to be a purchaser of a CEEC data structure, wherein the first request is for the CEEC data structure; Including a first selection criterion for selecting a seller;
Receiving a second request from the selling system that owns the CEEC data structure requesting to become the selling side of the CEEC data structure, the second request selecting the purchase side of the CEEC data structure; Including a second selection criterion for
Pairing the provider system and the selling system based on the first and second selection criteria;
Migrating the CEEC data structure from the selling system to the provider system;
And at least one memory including instructions that cause the at least one processor to execute a workload on the provider system in accordance with the conditions associated with the migrated CEEC data structure. A method for use in a data processing system including at least one computing device and a plurality of computing resources, the method for managing use of the computing resources, comprising:
A computing environment entitlement agreement (CEEC) manager to generate one or more CEECs, each CEEC including a plurality of conditions, wherein the conditions of the CEEC are specified for a specified period of time. Specifying the set of computing resources with the specified configuration that are to be used by the contracting parties for specified purposes at the level and intensity of the pattern;
The creation of one or more cohort profiles by a cohort and profile manager, each cohort profile containing the common of that cohort that each member of that cohort includes to associate with one cohort Specifying the characteristics,
A cohort management service defines a computing resource cohort that represents a group of computing resources from the plurality of computing resources based on one or more profiles generated by the cohort and profile manager Defining a CEEC cohort representing a group of CEECs based on one or more profiles generated by the cohort and profile manager;
The database system associates data corresponding to the plurality of computing resources, the one or more CEECs, and computing resources and CEECs based on the defined computing resource cohorts and CEEC cohorts. Storing the method. Based on an analysis of computing resource usage metric information collected from the computing resources associated with the computing resource cohort for each CEEC associated with the CEEC cohort by a usage scoring service 68. The method of claim 66, further comprising generating a score value.   Based on the score value generated by the usage scoring service, the cohort management service is responsible for computing resource membership associated with the computing resource cohort and CEEC associated with the CEEC cohort. 68. The method of claim 67, wherein membership is managed. 69. The method of claims 66-68, further comprising executing a transaction related to the CEEC market to exchange the CEEC between the buying computing resource and the selling computing resource by a CEEC market maker service. The method according to any one.   A buyer computing resource cohort in which the buyer computing resource has computing resources that can be used to satisfy one or more conditions of a CEEC sold by the seller computing resource The selling computing resource includes computing that the conditions of the CEEC to be sold are not satisfied by usage of the computing resource associated with the selling computing resource cohort 70. The method of claim 69, comprising a selling computing resource cohort indicated by the resource usage metric information. Using a transaction builder user interface to specify a transaction profile by a user, wherein the transaction profile includes a buyer computing resource for executing an instance of the transaction profile; and 71. A method according to any of claims 66 to 70, further comprising specifying characteristics of the selling computing resource.   72. The method of claim 71, wherein the CEEC market maker service selects the buyer computing resource and the seller computing resource according to the characteristics of the buyer computing resource and the seller computing resource. .   A user specifies a business goal criterion for the transaction profile using the user interface of the transaction builder, and the business goal criterion authorizes completion of the execution of an instance of the transaction profile 73. A method according to claim 71 or claim 72, wherein:   The CEEC market maker service determines a cost associated with the transaction-designated instance based on the business goal criteria, and the CEEC market maker service is based on the cost associated with the instance of the transaction. 74. A method according to any of claims 71 to 73, wherein it is determined whether to allow completion of the instance of the transaction designation. Each of the one or more sites of the organization includes a plurality of computing resources, and the one or more information technology management warehouse systems associated with the one or more sites of the organization provide the one 75. A method according to any of claims 66 to 74, further comprising collecting computing resource usage metric information from the plurality of computing resources at a site with which or information technology warehouse systems are associated. . 76. A method according to any of claims 66 to 75, further comprising routing a workload to a computing resource based on an association of the CEEC cohort to the computing resource cohort by a workload management service. . Interfacing the database system with an existing organizational computing system that provides information about the organizational structure of the organization by means of an interface, the CEEC and the computing resource cohort of the CEEC cohort Using the interface such that an item related to the database system corresponding to the computing resource of the information includes information about the organizational structure of the organization obtained from the existing organizational computing system. 77. A method according to any of claims 66 to 76, further comprising being updated. A device for managing the use of computing resources,
A computing environment entitlement agreement (CEEC) manager operable to generate one or more CEECs, each CEEC including a plurality of conditions, wherein the conditions of the CEEC are for a specified period of time, A Computing Environment Entitlement Contract (CEEC) Manager that specifies a set of computing resources with a specified configuration that is to be used by a contracting party for a specified purpose at a specified level and pattern strength When,
A cohort and profile manager operable to generate one or more cohort profiles, each cohort profile including each member of that cohort to associate with one cohort Cohorts and profile managers that specify common characteristics of
Defining a computing resource cohort representing a group of computing resources from the plurality of computing resources based on one or more profiles generated by the cohort and profile manager; and A cohort management service operable to define a CEEC cohort representing a group of CEECs based on one or more profiles generated by the profile manager;
For storing data corresponding to the plurality of computing resources, the one or more CEECs, and an association of computing resources and CEECs based on the defined computing resource cohorts and CEEC cohorts A device that includes a database system.   All steps of any one of claims 1 to 12, claim 14 to 25, claim 27 to 38, claim 40 to 51, claim 53 to 64, and claim 66 to 77 are performed. A computer program comprising adapted program code means.

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