CN119473334B - Implementation method and system for multi-dimensional management and automated deployment - Google Patents
Implementation method and system for multi-dimensional management and automated deployment Download PDFInfo
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Abstract
The application relates to a method and a system for realizing multidimensional management and automatic deployment. The method comprises the steps of searching physical servers in a network, obtaining hardware information of the physical servers by utilizing a preset protocol based on search results of the physical servers in the network, registering resources based on the hardware information, carrying out grouping management on the physical servers according to at least one dimension in preset hardware performance, geographic position and network topology based on the result of the resource registration, realizing multi-dimensional screening and distribution, utilizing the result of the multi-dimensional screening and distribution to implement automatic deployment of an operating system and software on the physical servers by utilizing a preset PXE technology, and utilizing the result of the automatic deployment of the operating system and the software to carry out dynamic resource distribution and scheduling based on a preset scheduling algorithm. The scheme of the application is beneficial to realizing the rapid allocation, deployment and management of physical resources and improving the utilization rate of the resources and the operation and maintenance efficiency.
Description
Technical Field
The invention relates to the field of computer networks and cloud computing, in particular to a method and a system for realizing multidimensional management and automatic deployment.
Background
In the present digital age, cloud computing and virtualization technology has become the core driving forces for the transformation of enterprise information technology architecture. Cloud computing provides computing resources, is flexible and extensible and is convenient in service mode according to the requirements, and the IT operation mode of an enterprise is greatly changed. The virtualization technology is used as an important support of cloud computing, and by constructing a virtual layer on physical hardware, computing, storage and network resources are abstracted into virtualized resources, so that efficient sharing and flexible allocation of the resources are realized.
Currently, the mainstream cloud computing solutions on the market, such as Amazon Web Services (AWS), microsoft Azure and Gu Geyun platform (GCP), all use virtualization technology widely. The schemes show various advantages in a general computing scene, such as quick creation and destruction of virtual machine examples, flexible allocation of resources is realized, enterprises can flexibly adjust the resource allocation according to actual service demands, and hardware purchasing cost and operation and maintenance management difficulty are effectively reduced. Meanwhile, a cloud computing platform based on virtualization provides a unified management interface and rich APIs, so that the usability and the automation degree of resource management are greatly improved.
However, in certain specific fields and high performance computing scenarios, virtualization techniques expose significant performance bottlenecks. For example, in the field of High Performance Computing (HPC), scientific research and engineering computing tasks are often extremely demanding in terms of computing performance, requiring full utilization of the full computing power of the physical hardware. The additional abstraction layer introduced by virtualization may result in a certain performance overhead, such as reduced execution efficiency of CPU instructions, increased memory access delay, and network and storage IO performance loss. In the big data processing scene, the real-time processing and analysis of mass data have extremely high requirements on the calculation, storage and network performance of the system, and the performance loss caused by the virtualization technology may cause the significant extension of the task execution time, so that the requirement of the service on timeliness cannot be met.
With the acceleration of enterprise digital transformation, the demand for direct management and efficient utilization of physical server resources is increasingly highlighted. On the one hand, when an enterprise processes a high-performance computing task, a solution capable of directly accessing physical hardware resources is urgently needed, so that performance cost caused by virtualization is reduced, and the extremely good performance of hardware is fully exerted. On the other hand, with the continuous expansion of the data center, the number of servers increases dramatically, and the conventional manual management method has difficulty in coping with the complex operation and maintenance management challenges. An enterprise needs an automatic physical server resource management system, so that centralized management of a plurality of physical servers, batch operating system installation, rapid application deployment, real-time resource monitoring and intelligent scheduling are realized, the resource delivery speed and the operation and maintenance efficiency are improved, and the management cost is reduced. In addition, in a multi-tenant environment, ensuring resource isolation and security access control between tenants, and realizing high availability and flexible expansibility of the system are also important requirements facing current enterprises.
In summary, the limitations of the existing cloud computing technology in a specific scenario bring about innovative demands for physical server resource management and automated deployment technologies, and the present invention aims to fill the technical gap and provide an efficient, flexible and reliable physical server resource management solution for enterprises.
Disclosure of Invention
In view of the above, the present invention is directed to a method and a system for implementing multidimensional management and automated deployment, so as to solve the related technical problems in the prior art.
According to a first aspect of an embodiment of the present invention, there is provided a method for implementing multidimensional management and automated deployment, the method comprising:
searching a physical server in a network;
acquiring hardware information of a physical server in the network by using a preset protocol based on a search result of the physical server in the network, and registering resources based on the hardware information;
based on the result of resource registration, physical servers are grouped and managed according to at least one dimension in preset hardware performance, geographic position and network topology, so that multidimensional screening and distribution are realized;
Utilizing the multi-dimensional screening and distributing results, and utilizing a preset PXE technology to implement automatic deployment of an operating system and software on the physical server;
and utilizing the results of automatic deployment of the operating system and the software to dynamically allocate and schedule the resources based on a preset scheduling algorithm.
Further, the searching result based on the physical server in the network, acquiring the hardware information of the physical server by using a preset protocol, and registering the resource based on the hardware information, includes:
Based on the search result of the physical server in the network, acquiring the hardware information of the physical server by utilizing a predefined discovery mode in a preset PXE technology and IPMI and Redfish standard protocols;
storing the hardware information of the physical servers to a preset end through a preset RESTful interface, and generating a unique identifier UUID for each physical server;
The hardware information of the physical server comprises a processor model number, a core number, a memory capacity, a memory type, a disk capacity, an interface type and network interface information.
Further, the grouping management of the physical servers according to at least one dimension in the preset hardware performance, geographic location and network topology based on the result of the resource registration, to realize multi-dimensional screening and distribution, includes:
and carrying out grouping management on the physical servers according to at least one dimension in the preset hardware performance, geographic position and network topology, and acquiring grouping information of marking resources by a user through an API or according to a custom grouping rule form so as to realize multi-dimension screening and distribution.
Further, the automatically deploying the operating system and the software on the physical server by using the preset PXE technology according to the result of the multi-dimensional screening and distribution includes:
guiding a target physical server by using PXE and loading a lightweight guiding operation system;
acquiring an installation image of the target operating system in a preset image storage library, and executing installation and initialization configuration of the target operating system by using a preset automatic installation tool by using the installation image.
Further, the dynamic allocation and scheduling of resources based on a preset scheduling algorithm by using the results of the automatic deployment of the operating system and the software includes:
monitoring the resource utilization rate of each physical server by using a preset load balancing algorithm, and preferentially distributing nodes with lower loads;
and utilizing a preset affinity scheduling algorithm to allocate related tasks or related services to specific nodes according to the association relation between the nodes or business logic requirements among the physical servers.
According to a second aspect of the embodiments of the present invention, there is provided a system for implementing multidimensional management and automation deployment, which is applied to the method for implementing multidimensional management and automation deployment described in any one of the above, the system comprising:
the acquisition module is used for searching a physical server in the network;
The first processing module is used for acquiring hardware information of a physical server in the network by utilizing a preset protocol based on a search result of the physical server in the network and registering resources based on the hardware information;
The second processing module is used for carrying out grouping management on the physical servers according to at least one dimension in the preset hardware performance, geographic position and network topology based on the result of resource registration, so as to realize multi-dimensional screening and distribution;
the third processing module is used for utilizing the multi-dimensional screening and distribution results and utilizing a preset PXE technology to implement automatic deployment of an operating system and software on the physical server;
And the fourth processing module is used for dynamically distributing and scheduling resources based on a preset scheduling algorithm by utilizing the results of automatic deployment of the operating system and the software.
Further, the system further comprises:
the life cycle management module is used for carrying out full life cycle monitoring on the physical server;
The life cycle management module comprises:
The online unit is used for automatically entering a hardware discovery and registration process when a new physical server is accessed into the system;
the monitoring unit is used for collecting the performance index of the physical server in real time and providing a visual chart;
the fault processing unit is used for automatically detecting hardware faults and carrying out hardware diagnosis through a preset algorithm;
and the offline unit is used for automatically clearing the stored data of the server and removing the stored data from the resource pool when the server needs to be retired.
Further, the system further comprises:
the security and authority control module comprises:
The user authentication unit is used for authenticating a user to be used based on a preset authentication mode, wherein the preset authentication mode comprises local user authentication, LDAP integrated authentication and OAuth authentication;
The authority control unit is used for dividing the users into different roles based on the access control of the roles and distributing corresponding authorities according to the roles;
and the data security unit is used for carrying out encryption initialization on the preset storage medium and encrypting preset API communication by using the TLS encryption protocol.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
It can be understood that the technical scheme provided by the application automatically discovers the physical server through a standard protocol, classifies and resource-based management of hardware information, adopts a mirror image storage library and a templated configuration file to realize a technical scheme of batch deployment, and supports an implementation mechanism of integrated automatic deployment of an operating system and a specific application program environment. Based on the application of the intelligent scheduling algorithm of load balancing and affinity in the resource allocation of the physical server, the physical server resource is dynamically adjusted to meet the real-time load change.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram illustrating an implementation of multidimensional management and automation deployment in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating the implementation system components of a multidimensional management and automation deployment in accordance with an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Example 1
Referring to fig. 1, fig. 1 is a flow chart illustrating a method for implementing multidimensional management and automation deployment according to an exemplary embodiment, the method includes:
S1, searching a physical server in a network;
s2, acquiring hardware information of a physical server by using a preset protocol based on a search result of the physical server in the network, and registering resources based on the hardware information;
S3, based on a result of resource registration, carrying out grouping management on physical servers according to at least one dimension in preset hardware performance, geographic position and network topology, and realizing multi-dimensional screening and distribution;
S4, utilizing the multi-dimensional screening and distribution results, and utilizing a preset PXE technology to implement automatic deployment of an operating system and software on the physical server;
S5, utilizing the results of automatic deployment of the operating system and the software, and carrying out dynamic allocation and scheduling of resources based on a preset scheduling algorithm.
In a specific implementation, please refer to step SI-S2, based on the search result of the physical server in the network, the hardware information of the physical server is obtained by using a predefined discovery mode in a preset PXE technology and IPMI, redfish standard protocols;
And storing the hardware information of the physical servers to a preset end through a preset RESTful interface, and generating a unique identifier UUID for each physical server for subsequent inquiry and management, wherein the preset end comprises resource management data.
The hardware information of the physical server comprises a processor model number, a core number, a memory capacity, a memory type, a disk capacity, an interface type and network interface information.
In a specific implementation, as described in step S3, the step of grouping management, based on the result of the resource registration, on the physical servers according to at least one dimension of a preset hardware performance, a preset geographic location, and a preset network topology, to implement multidimensional screening and allocation includes:
and carrying out grouping management on the physical servers according to at least one dimension in the preset hardware performance, geographic position and network topology, and acquiring grouping information of marking resources by a user through an API or according to a custom grouping rule form so as to realize multi-dimension screening and distribution.
More specifically, the present invention provides a flexible and multidimensional grouping mechanism in physical server resource management. In terms of hardware characteristics, the characteristics of whether a server is a high-performance computing node or a mass storage node are classified. The servers are grouped according to information such as a rack number, a data center position and the like from a physical location dimension. In large data centers, servers located on the same rack may have similar network latency and physical connection characteristics, and their packet management helps optimize local network communications and resource allocation. For example, servers in the same cabinet are grouped into a group, so that unified power management, heat dissipation management and network topology planning are conveniently carried out for the group of servers. For servers distributed at different data center positions, the servers are grouped according to the geographic position of the data center, network access conditions and other factors, so that reasonable decisions can be made in the scheduling of resources and service deployment across the data centers.
Based on traffic demand is also an important grouping basis. The servers bearing the database service are classified into database server groups, the servers concentrate on data storage, inquiry and management, and have higher requirements on the consistency, reliability and read-write performance of the data, and the application server groups are responsible for running various business application programs, provide service interfaces for users, and have good processing capacity and response speed so as to meet the requirement of concurrent access of a large number of users. Through the grouping based on the service requirements, the performance and management requirements under different service scenes can be better met.
Users possess a high degree of autonomy in the resource management process. Through an API or a management platform, a user can customize grouping rules according to own business logic and management requirements. For example, an enterprise may define grouping rules for servers according to project names or department names, divide servers associated with a particular project into the same group for individual management and monitoring of its resources by a project team, or group the servers according to hardware procurement batches of servers to enable targeted operations during hardware maintenance and upgrades.
In tagging resources, a user may add multiple tags to each server to describe its various attributes.
In one embodiment, as described in step S4, the automatically deploying the operating system and software on the physical server by using the preset PXE technology according to the result of the multi-dimensional screening and allocation includes:
guiding a target physical server by using PXE and loading a lightweight guiding operation system;
acquiring an installation image of the target operating system in a preset image storage library, and executing installation and initialization configuration of the target operating system by using a preset automatic installation tool by using the installation image.
In particular implementations, at an initial stage of an operating system automation deployment, PXE (Preboot Execution Environment) techniques are utilized to boot a target server to boot. PXE boot allows a server to obtain information and files needed for startup over a network without installing an operating system on a local storage device (e.g., hard disk). When the target server starts up, it will send a broadcast request looking for a PXE server in the network. After receiving the request, the PXE server sends a start file to the target server according to a pre-configured boot policy, and the boot server enters a pre-defined discovery mode. In this mode, the server will load a lightweight boot operating system, such as TinyCore Linux. TinyCore Linux is small in size and high in starting speed, and can provide a basic operation environment, so that the server has enough network functions to acquire a complete operating system installation image from a network later. The lightweight operating system runs in the memory, does not depend on local hard disk storage, and lays a foundation for an automatic deployment process.
Secondly, after the target server loads the lightweight boot operating system, the target server is connected to the mirror image storage library through a network. The image repository stores installation images of various mainstream operating systems, such as Ubuntu, centOS, windows Server, etc. And the server downloads the corresponding installation image from the image storage library according to a preconfigured deployment task or an operating system version specified by a user. This process involves a network transfer protocol such as HTTP, FTP, TFTP, or the like to ensure that the installation image is transferred accurately and quickly to the target server. The image repository can be a server in a local network or a storage service located in the cloud, is designed to provide centralized operating system image management, is convenient to quickly acquire required operating system resources in a large-scale server deployment scene, and ensures deployment consistency and repeatability.
Finally, after the target operating system installation image is downloaded to the server, the automated installation tool begins to function. Tools such as KICKSTART (mainly used for Red Hat series operating systems) and Cloud-Init (applicable to various Linux releases and Cloud environments) can read predefined configuration files containing various parameters in the operating system installation process, such as disk partition schemes, user account creation, software package selection, and the like. The automatic installation tool automatically executes the installation process of the operating system according to the instruction of the configuration file, wherein the installation process comprises operations of formatting a disk, copying a system file, installing a driver program and the like. After the installation is completed, the configuration is initialized according to the configuration, such as setting network parameters (IP address, subnet mask, gateway, etc.), configuring firewall rules, installing common software packages, etc. The series of automatic operations ensure that the installation process of the operating system does not need manual intervention, greatly improves the deployment efficiency, ensures the consistency of the deployment of a plurality of servers, reduces the possibility of human errors, and is suitable for scenes such as a large-scale data center, a cloud computing environment and the like which need to rapidly deploy a large number of servers.
In specific implementation, a plurality of predefined templates are also provided, including common operating systems, database environments (such as MySQL, postgreSQL), application frameworks (such as Django and node. Js), and users can customize the templates to realize batch deployment consistency.
In one embodiment, as shown in step S5, the dynamic allocation and scheduling of resources based on a preset scheduling algorithm using the results of the automatic deployment of the operating system and the software includes:
monitoring the resource utilization rate of each physical server by using a preset load balancing algorithm, and preferentially distributing nodes with lower loads;
and utilizing a preset affinity scheduling algorithm to allocate related tasks or related services to specific nodes according to the association relation between the nodes or business logic requirements among the physical servers.
In specific implementation, the system uses a preset load balancing algorithm to realize efficient allocation of physical server resources. The load condition of the server is mastered in real time by continuously monitoring the resource utilization rate of each physical server, including key indexes such as CPU utilization rate, memory occupancy rate, I/O operation frequency and the like. For example, at regular intervals (e.g., seconds or minutes), the system may collect resource usage data for each server. Based on the data, the load balancing algorithm calculates the load weight or load level of each server to determine its load level. When a new task or service request arrives, the system will preferentially allocate it to the node with the lower load. The allocation strategy is realized based on various load balancing algorithms, such as a Round-Robin algorithm (Round-Robin), which sequentially allocates tasks to each server to ensure that each server can obtain balanced task allocation, a Weighted Round-Robin algorithm (Weighted Round-Robin) allocates different weights to servers according to performance differences (such as CPU core number and memory size) of the servers, the servers with high performance can bear more tasks, and a minimum connection algorithm (Least-connection) allocates new tasks to the server with the Least current connection number to balance the workload of each server. Through comprehensive application of the algorithms, the system can dynamically adapt to the change of server load, effectively avoid performance degradation caused by overload of individual servers, improve the resource utilization rate and response speed of the whole system, and is particularly suitable for scenes of processing a large number of concurrent tasks, such as a high-flow Web application server cluster, a large-scale data processing center and the like.
Furthermore, the system intelligently distributes related tasks or services to specific nodes according to the association relation between the nodes or the business logic requirement by means of a preset affinity scheduling algorithm. In terms of node associations, hardware affinity between servers is considered, for example, for tasks that require frequent data exchanges, if servers are connected through a high-speed network (such as InfiniBand) or share specific hardware resources (such as high-performance storage devices), these tasks are allocated to server nodes with affinity to reduce data transfer delay and improve overall system performance. From the aspect of service logic requirements, scheduling is performed according to different service application scenes. For example, for tasks that require access to a particular data storage area or rely on a particular software environment, they are assigned to server nodes that match it. The affinity scheduling algorithm may also consider the relevance between tasks, such as assigning tasks belonging to the same business process or having data dependencies to similar nodes, to reduce communication overhead and coordination costs between tasks. The scheduling strategy improves the system performance and enhances the stability and reliability of service application, and is suitable for the scenes of enterprise-level application systems, distributed computing environments, service deployment in cloud computing platforms and the like with higher requirements on performance and data consistency.
In one embodiment, the resource utilization rate (such as a CPU, a memory, and an IO) of each physical server is continuously monitored through a scheduling algorithm based on load balancing, so that tasks can be accurately distributed to nodes with lower loads, and resource idling and waste are avoided. In an actual application scene, for example, in a data center for processing a plurality of computing tasks at the same time, the system can intelligently distribute new tasks to relatively idle servers according to the real-time load condition of the servers, so that the resources of each server can be fully utilized, the utilization rate of the whole resources is improved, the distributed nodes are ensured to meet the service logic requirements based on an affinity-based scheduling strategy, the data transmission time is shortened, the response speed of the system is improved, the resource allocation is further optimized, and the utilization efficiency of the resources is improved.
In one embodiment, the method further comprises life cycle management, when a new physical server is accessed into the system, automatically entering a hardware discovery and registration process, and collecting performance indexes (such as CPU temperature, fan rotation speed and power consumption) of the physical server in real time and providing a visual chart.
Furthermore, the system also provides a fault processing function, and hardware faults are automatically detected and hardware diagnosis is carried out through a preset algorithm, specifically, the hardware faults (such as disk damage and memory errors) are automatically detected, and remote restarting and hardware diagnosis (through IPMI) are supported.
Further, an offline related service is provided, and when the server needs to be retired, the stored data of the server is automatically cleared and removed from the resource pool.
In the specific implementation, the full life cycle automatic management from the online, monitoring and fault processing to the offline of the physical server is realized. Hardware discovery and registration are automatically completed when a server is online, server performance indexes (such as CPU temperature, fan rotation speed and power consumption) are monitored in real time, remote restarting and hardware diagnosis are automatically detected and supported when hardware faults (such as disk damage and memory errors) occur, and stored data is automatically cleared and removed from a resource pool when the server is offline. The series of automatic operations greatly reduce manual intervention, reduce the working intensity of operation and maintenance personnel, improve the operation and maintenance efficiency, and simultaneously reduce errors possibly caused by manual operation.
Further, a security and authority control module is also provided, including:
the system is self-built with a user account and a password database, and is suitable for the scenes of independence and small user quantity when the user logs in, LDAP integrated authentication and OAuth authentication.
And the RBAC is adopted to divide users into roles such as an administrator, a common user and the like. The manager has comprehensive system management authority, such as user management, system configuration, data maintenance, etc., and the common user has only basic business operation authority, such as data viewing and partial information modification. The authority allocation ensures that a user can only operate in an authorized range according to a minimum authority principle, ensures the safety and data integrity of the system, prevents illegal operation and data leakage, and is suitable for various systems needing strict authority management.
In addition, when the operating system is deployed, the storage medium is encrypted and initialized, all API communication is encrypted by using TLS, and data transmission security is ensured.
Furthermore, each functional module of the system operates independently, such as hardware discovery, deployment, monitoring and other modules do not interfere with each other. This allows each module to be developed, tested, updated independently, without affecting each other. For example, the support of the hardware discovery module to new hardware is improved, other modules are not affected, the function expansion and optimization are convenient, the requirement change in the development of enterprises can be flexibly dealt with, and the method is suitable for the scene of continuous expansion of business and continuous updating of technology.
In addition, a rich RESTful interface is provided to facilitate integration with other systems, such as IT service management tools, monitoring tools, etc. The enterprise can construct a unified management platform by the method, so that data sharing and collaborative work are realized. By taking the integration of the monitoring tool as an example, the system monitoring data can be transmitted in real time, so that centralized monitoring and management are facilitated, the management efficiency and decision scientificity are improved, and the system monitoring system is suitable for the multi-system cooperation requirement in a complex enterprise environment.
Further, the user may develop custom plug-ins to extend specific scene functionality. Such as enterprise specific business logic or special hardware adaptations, may be implemented by plug-ins. The plug-in mechanism enriches system functions, meets diversified personalized requirements, does not influence the stability of a system core architecture, enhances the adaptability and competitiveness of the system, and is suitable for enterprises or innovative projects with unique service requirements.
According to the technical scheme, the automatic operating system installation and software deployment of the target Server are supported by means of the predefined templates and the configuration files, the automatic operating system installation and software deployment comprise a mainstream Linux distribution board, a Windows Server and the like, a plurality of predefined templates (such as a common operating system, a database environment and an application framework) are also provided, and a user can customize the templates to achieve batch deployment consistency. The rapid deployment capability is particularly important in a large-scale server deployment scene, for example, when a large number of servers are required to be rapidly on line for new expansion business of enterprises, the deployment time can be obviously shortened, the resource delivery speed can be improved, and the enterprises can respond to market demands more rapidly.
Referring to fig. 2, fig. 2 is a schematic diagram illustrating an implementation system of multidimensional management and automation deployment, the system comprising:
an acquisition module 20 for searching for a physical server in the network;
a first processing module 21, configured to obtain, based on a search result of a physical server in the network, hardware information of the physical server using a preset protocol, and perform resource registration based on the hardware information;
The second processing module 22 is configured to perform packet management on the physical servers according to at least one dimension in a preset hardware performance, a geographic location, and a network topology based on a result of resource registration, so as to implement multidimensional screening and allocation;
the third processing module 23 is configured to implement automatic deployment of an operating system and software on the physical server by using a preset PXE technology according to the results of the multi-dimensional screening and allocation;
and a fourth processing module 24, configured to dynamically allocate and schedule resources based on a preset scheduling algorithm by using the results of the automated deployment of the operating system and the software.
The system further comprises:
the life cycle management module is used for carrying out full life cycle monitoring on the physical server;
The life cycle management module comprises:
The online unit is used for automatically entering a hardware discovery and registration process when a new physical server is accessed into the system;
the monitoring unit is used for collecting the performance index of the physical server in real time and providing a visual chart;
the fault processing unit is used for automatically detecting hardware faults and carrying out hardware diagnosis through a preset algorithm;
and the offline unit is used for automatically clearing the stored data of the server and removing the stored data from the resource pool when the server needs to be retired.
The system further comprises:
the security and authority control module comprises:
The user authentication unit is used for authenticating a user to be used based on a preset authentication mode, wherein the preset authentication mode comprises local user authentication, LDAP integrated authentication and OAuth authentication;
The authority control unit is used for dividing the users into different roles based on the access control of the roles and distributing corresponding authorities according to the roles;
and the data security unit is used for carrying out encryption initialization on the preset storage medium and encrypting preset API communication by using the TLS encryption protocol.
It can be understood that the technical scheme provided by the application automatically discovers the physical server through a standard protocol, classifies and resource-based management of hardware information, adopts a mirror image storage library and a templated configuration file to realize a technical scheme of batch deployment, and supports an implementation mechanism of integrated automatic deployment of an operating system and a specific application program environment. Based on the application of the intelligent scheduling algorithm of load balancing and affinity in the resource allocation of the physical server, the physical server resource is dynamically adjusted to meet the real-time load change.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality" means at least two.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (7)
1. A method for implementing multidimensional management and automated deployment, the method comprising:
searching a physical server in a network;
acquiring hardware information of a physical server in the network by using a preset protocol based on a search result of the physical server in the network, and registering resources based on the hardware information;
based on the result of resource registration, physical servers are grouped and managed according to at least one dimension in preset hardware performance, geographic position and network topology, so that multidimensional screening and distribution are realized;
Utilizing the multi-dimensional screening and distributing results, and utilizing a preset PXE technology to implement automatic deployment of an operating system and software on the physical server;
Utilizing the results of automatic deployment of an operating system and software to dynamically allocate and schedule resources based on a preset scheduling algorithm;
The searching result based on the physical server in the network, acquiring the hardware information of the physical server by using a preset protocol, and registering the resource based on the hardware information, includes:
Based on the search result of the physical server in the network, acquiring the hardware information of the physical server by utilizing a predefined discovery mode in a preset PXE technology and IPMI and Redfish standard protocols;
storing the hardware information of the physical servers to a preset end through a preset RESTful interface, and generating a unique identifier UUID for each physical server;
The hardware information of the physical server comprises a processor model number, a core number, a memory capacity, a memory type, a disk capacity, an interface type and network interface information.
2. The method of claim 1, wherein the grouping management of physical servers based on the result of the resource registration according to at least one dimension of a preset hardware performance, a geographic location, and a network topology, and implementing multidimensional screening and allocation, comprises:
and carrying out grouping management on the physical servers according to at least one dimension in the preset hardware performance, geographic position and network topology, and acquiring grouping information of marking resources by a user through an API or according to a custom grouping rule form so as to realize multi-dimension screening and distribution.
3. The method of claim 1, wherein the utilizing the results of the multi-dimensional screening and assignment to implement an automated deployment of operating systems and software to the physical servers using a pre-set PXE technique comprises:
guiding a target physical server by using PXE and loading a lightweight guiding operation system;
Acquiring an installation image of a target operating system in a preset image storage library, and executing installation and initialization configuration of the target operating system by using a preset automatic installation tool by using the installation image.
4. The method of claim 1, wherein the dynamically allocating and scheduling resources based on a preset scheduling algorithm using results of an automated deployment of an operating system and software comprises:
monitoring the resource utilization rate of each physical server by using a preset load balancing algorithm, and preferentially distributing nodes with lower loads;
and utilizing a preset affinity scheduling algorithm to allocate related tasks or related services to specific nodes according to the association relation between the nodes or business logic requirements among the physical servers.
5. A system for implementing multidimensional management and automation deployment, applied to the method for implementing multidimensional management and automation deployment according to any one of claims 1 to 4, characterized in that it comprises:
the acquisition module is used for searching a physical server in the network;
The first processing module is used for acquiring hardware information of a physical server in the network by utilizing a preset protocol based on a search result of the physical server in the network and registering resources based on the hardware information;
The searching result based on the physical server in the network, acquiring the hardware information of the physical server by using a preset protocol, and registering the resource based on the hardware information, includes:
Based on the search result of the physical server in the network, acquiring the hardware information of the physical server by utilizing a predefined discovery mode in a preset PXE technology and IPMI and Redfish standard protocols;
storing the hardware information of the physical servers to a preset end through a preset RESTful interface, and generating a unique identifier UUID for each physical server;
The hardware information of the physical server comprises processor model and core number, memory capacity and type, disk capacity and interface type and network interface information;
The second processing module is used for carrying out grouping management on the physical servers according to at least one dimension in the preset hardware performance, geographic position and network topology based on the result of resource registration, so as to realize multi-dimensional screening and distribution;
the third processing module is used for utilizing the multi-dimensional screening and distribution results and utilizing a preset PXE technology to implement automatic deployment of an operating system and software on the physical server;
And the fourth processing module is used for dynamically distributing and scheduling resources based on a preset scheduling algorithm by utilizing the results of automatic deployment of the operating system and the software.
6. The system of claim 5, wherein the system further comprises:
the life cycle management module is used for carrying out full life cycle monitoring on the physical server;
The life cycle management module comprises:
The online unit is used for automatically entering a hardware discovery and registration process when a new physical server is accessed into the system;
the monitoring unit is used for collecting the performance index of the physical server in real time and providing a visual chart;
the fault processing unit is used for automatically detecting hardware faults and carrying out hardware diagnosis through a preset algorithm;
and the offline unit is used for automatically clearing the stored data of the server and removing the stored data from the resource pool when the server needs to be retired.
7. The system of claim 5, wherein the system further comprises:
the security and authority control module comprises:
The user authentication unit is used for authenticating a user to be used based on a preset authentication mode, wherein the preset authentication mode comprises local user authentication, LDAP integrated authentication and OAuth authentication;
The authority control unit is used for dividing the users into different roles based on the access control of the roles and distributing corresponding authorities according to the roles;
and the data security unit is used for carrying out encryption initialization on the preset storage medium and encrypting preset API communication by using the TLS encryption protocol.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101969391A (en) * | 2010-10-27 | 2011-02-09 | 北京邮电大学 | Cloud platform supporting fusion network service and operating method thereof |
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| EP3721350A4 (en) * | 2017-12-08 | 2021-12-29 | Net-Thunder, LLC | Automatically deployed information technology (it) system and method |
| CN113553194B (en) * | 2021-09-22 | 2022-05-06 | 阿里巴巴达摩院(杭州)科技有限公司 | Hardware resource management method, device and storage medium |
| CN117040999A (en) * | 2023-08-18 | 2023-11-10 | 中科芯集成电路有限公司 | Remote control method for physical server |
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| CN101969391A (en) * | 2010-10-27 | 2011-02-09 | 北京邮电大学 | Cloud platform supporting fusion network service and operating method thereof |
| CN104272292A (en) * | 2012-03-12 | 2015-01-07 | 铃盛公司 | Network resource deployment for cloud-based services |
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