CN118170541A - Cloud platform resource adjustment method, device, equipment, medium and program product - Google Patents

Abstract

The disclosure provides a cloud platform resource adjustment method, a device, equipment, a storage medium and a program product, which can be applied to the technical field of cloud computing, the technical field of financial science and technology and other technical fields. The cloud platform resource adjustment method comprises the following steps: determining a cloud platform to be processed in response to the presence of the resource data abnormality; acquiring a resource data set of a cloud platform to be processed and a current running environment of the cloud platform to be processed; and determining an adjustment strategy of the cloud platform to be processed based on the resource data set and the current running environment, and adjusting the resources of the cloud platform to be processed based on the adjustment strategy to obtain the target cloud platform.

Description

Cloud platform resource adjustment method, device, equipment, medium and program product

Technical Field

The present disclosure relates to the field of cloud computing, and more particularly, to a method, an apparatus, a device, a medium, and a program product for adjusting cloud platform resources.

Background

The cloud platform is used as a service platform based on cloud computing technology and provides a series of cloud computing resources and tools to help enterprises to perform tasks such as application development, data storage, data analysis, application program hosting and the like. The cloud platform can avoid the high cost of the enterprise self-building and maintenance server, and more enterprises select to utilize the cloud platform to develop the service. In order to further achieve the purpose of reducing the operation cost of the cloud platform, the cloud resource data needs to be monitored and adjusted regularly.

The existing cloud platform resource adjustment method generally comprises the following steps: based on the running state of the cloud platform monitoring management monitoring basic environment and the current use situation of cloud resources, an on-line engineer manually searches information such as computing resources, storage resource states and the like through cloud monitoring, an operation analysis report of the cloud platform resources is generated regularly, a cloud resource management scheme and a plan are formulated according to analysis results of operation data, and the cloud resource shortage problem is relieved through change. The problems of high labor cost, non-uniform cloud platform supervision, non-objective adjustment strategy formulation and the like exist.

Disclosure of Invention

In view of the foregoing, the present disclosure provides a cloud platform adjustment method, apparatus, device, medium, and program product.

According to a first aspect of the present disclosure, there is provided a cloud platform adjustment method, including: determining a cloud platform to be processed in response to the presence of the resource data abnormality; acquiring a resource data set of a cloud platform to be processed and a current running environment of the cloud platform to be processed; and determining an adjustment strategy of the cloud platform to be processed based on the resource data set and the current running environment, and adjusting the resources of the cloud platform to be processed based on the adjustment strategy to obtain the target cloud platform.

According to an embodiment of the present disclosure, the method further comprises: acquiring and monitoring cloud platform resource data in real time through a data lake; and under the condition that abnormal resource data is monitored, determining the cloud platform corresponding to the resource data as a cloud platform to be processed.

According to an embodiment of the present disclosure, acquiring and monitoring cloud platform resource data in real time by a data lake includes: identity authentication is carried out based on an API key of the cloud platform; after the identity authentication is passed, sending a data acquisition request to the cloud platform through an API interface; and analyzing the API response to acquire the cloud platform resource data, wherein the API response is generated by the cloud platform based on the data acquisition request.

According to an embodiment of the present disclosure, obtaining a resource data set of a cloud platform to be processed and a current operating environment of the cloud platform to be processed includes: determining a cloud platform to be processed based on data information of the abnormal data; inquiring all resource data of the cloud platform to be processed in a data lake to obtain a resource data set; and acquiring the current running environment from the cloud platform to be processed based on the query command.

According to an embodiment of the present disclosure, determining an adjustment policy for a cloud platform to be processed based on a resource dataset and a current operating environment includes: calculating a plurality of reports based on the resource data set, wherein the reports are used for reflecting the resource conditions of the cloud platform, and different reports correspond to different types of resource conditions; and determining an adjustment strategy of the cloud platform to be processed based on the current running environment of the cloud platform to be processed and the multiple reports.

According to an embodiment of the present disclosure, a plurality of reports are calculated based on a resource dataset, including: classifying data in the resource data set based on the resource type to obtain a plurality of data sets; the resource type at least comprises one of a CPU, a memory, a storage, a server and a system disk; respectively executing corresponding calculation operations on the data sets to obtain a plurality of reports; wherein the computing operations corresponding to the data sets of different resource types are different.

According to an embodiment of the present disclosure, determining an optimization strategy of a cloud platform based on a current environment of the cloud platform and a plurality of reports includes: determining a resource optimization direction based on the current running environment of the cloud platform to be processed; and determining an adjustment strategy according to the report and the resource optimization direction.

According to an embodiment of the present disclosure, adjusting a resource of a cloud platform to be processed based on an adjustment policy to obtain a target cloud platform includes: acquiring an instantiation association relationship between a virtual machine and a physical machine; and respectively adjusting the physical machine resources and the virtual machine resources based on the instantiation association relation and the adjustment strategy.

A second aspect of the present disclosure provides a cloud platform resource adjustment apparatus, including: the determining module is used for determining a cloud platform to be processed in response to the presence of the abnormality of the resource data; the acquisition module is used for acquiring a resource data set of the cloud platform to be processed and the current running environment of the cloud platform to be processed; and the adjustment module is used for determining an adjustment strategy of the cloud platform to be processed based on the resource data set and the current running environment, and adjusting the resources of the cloud platform to be processed based on the adjustment strategy to obtain the target cloud platform.

A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the cloud platform resource adjustment method described above.

A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described cloud platform resource adjustment method.

The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which when executed by a processor implements the above-mentioned cloud platform resource adjustment method.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an application scenario diagram of a cloud platform resource adjustment method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a cloud platform resource adjustment method according to an embodiment of the disclosure;

FIG. 3 schematically illustrates a flow chart of determining a cloud platform to process, according to an embodiment of the disclosure;

FIG. 4 schematically illustrates a flowchart of acquiring a resource dataset of a cloud platform to be processed and a current operating environment of the cloud platform to be processed, according to an embodiment of the disclosure;

FIG. 5 schematically illustrates a flow chart of determining an adjustment policy for a cloud platform to be processed, according to an embodiment of the disclosure;

FIG. 6 schematically illustrates a flow chart of determining an adjustment policy for a cloud platform to be processed, according to an embodiment of the disclosure;

FIG. 7 schematically illustrates a flow chart for adjusting resources of a cloud platform to be processed based on an adjustment policy according to an embodiment of the disclosure;

fig. 8 schematically illustrates a block diagram of a cloud platform resource adjustment device according to an embodiment of the present disclosure; and

Fig. 9 schematically illustrates a block diagram of an electronic device adapted to implement a cloud platform resource adjustment method according to an embodiment of the disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a convention should be interpreted in accordance with the meaning of one of skill in the art having generally understood the convention (e.g., "a system having at least one of A, B and C" would include, but not be limited to, systems having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

The embodiment of the disclosure provides a cloud platform resource adjustment method, which comprises the following steps: determining a cloud platform to be processed in response to the presence of the resource data abnormality; acquiring a resource data set of a cloud platform to be processed and a current running environment of the cloud platform to be processed; and determining an adjustment strategy of the cloud platform to be processed based on the resource data set and the current running environment, and adjusting the resources of the cloud platform to be processed based on the adjustment strategy to obtain the target cloud platform.

Fig. 1 schematically illustrates an application scenario diagram of a cloud platform resource adjustment method, apparatus, device, medium and program product according to an embodiment of the disclosure.

As shown in fig. 1, an application scenario 100 according to this embodiment may include a terminal device 101, a terminal device 102, a terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide communication links between the terminal device 101, the terminal device 102, the terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal device 101, the terminal device 102, the terminal device 103, to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on terminal devices 101, 102, 103.

Terminal device 101, terminal device 102, terminal device 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by the user using the terminal device 101, the terminal device 102, the terminal device 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that, the cloud platform resource adjustment method provided by the embodiments of the present disclosure may be generally executed by the server 105. Accordingly, the cloud platform resource adjustment device provided by the embodiments of the present disclosure may be generally disposed in the server 105. The cloud platform resource adjustment method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal device 101, the terminal device 102, the terminal device 103, and/or the server 105. Accordingly, the cloud platform resource adjustment apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal device 101, the terminal device 102, the terminal device 103, and/or the server 105.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The cloud platform resource adjustment method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 7 based on the scenario described in fig. 1.

Fig. 2 schematically illustrates a flowchart of a cloud platform resource adjustment method according to an embodiment of the present disclosure.

As shown in fig. 2, the cloud platform resource adjustment of this embodiment includes operations S210 to S230.

In operation S210, a pending cloud platform is determined in response to the presence of the resource data anomaly.

In some embodiments, resource data for multiple cloud platforms is stored and monitored based on a data lake. Under the condition that abnormal resource data are detected, determining a cloud platform to be processed from a plurality of cloud platforms based on the abnormal resource data, so as to adjust resources of the cloud platform to be processed, and improve performance of the cloud platform.

In the specific implementation process, the data lake is used for integrating and monitoring the resource data of the plurality of cloud platforms, unified management of the plurality of cloud platforms can be achieved, the cloud platforms needing to be subjected to resource adjustment are found timely and adjusted, and timeliness of the resource adjustment of the cloud platforms is improved.

In a specific implementation process, the resource data of the cloud platform may include specification information, mirror image information, physical host information, virtual machine information, and the like, for example. Further, the specification information may further include a CPU core number, a memory capacity, a storage capacity, and the like, the image information includes an operating system type, a version number, and the like, the physical host information includes a host name, an IP address, a memory usage, a CPU usage, a disk space usage, and the like, and the virtual machine information includes a virtual machine name, a memory capacity, a CPU usage, a memory usage, a disk space usage, and the like.

In operation S220, a resource data set of the cloud platform to be processed and a current operating environment of the cloud platform to be processed are acquired.

In some embodiments, after determining the cloud platform to be processed, a resource dataset of the cloud platform to be processed is obtained from a data lake to evaluate a current operating state of the cloud platform to be processed based on the resource dataset, wherein the resource dataset includes all resource data of the cloud platform. In addition, the current running environment of the cloud platform to be processed needs to be obtained.

The current operating environment may include, for example, an operating system, network bandwidth, latency, etc., and the operating environment of the cloud platform may affect a resource usage manner, user expectations, etc. For example, in the case of limited network bandwidth, transmission of large amounts of data may be limited, resulting in wasted resources; or when the network delay of the region where the cloud platform is located is high, the requirement of the user in the current region on response time may be lower than that of the user in other regions. Therefore, the accuracy of resource adjustment is improved by acquiring the current running environment of the cloud platform, so that the resource adjustment strategy is more in line with the current running environment and user expectations.

In operation S230, an adjustment policy of the cloud platform to be processed is determined based on the resource data set and the current operating environment, and the resource of the cloud platform to be processed is adjusted based on the adjustment policy, so as to obtain the target cloud platform.

In some embodiments, the adjustment strategy of the cloud platform to be processed is determined from the information of the two dimensions of the resource data set and the current running environment, so that the accuracy of the adjustment strategy of the cloud platform can be effectively improved, and the cloud platform resource is effectively optimized.

According to the embodiment of the disclosure, the cloud platform resource data is collected and managed based on the data lake, so that the resource data of different cloud platforms can be integrated, the resource monitoring across a plurality of cloud platforms is facilitated, and the monitoring and management flow of the cloud platforms is effectively simplified. After the cloud platforms to be processed exist in the plurality of cloud platforms, the adjustment strategy of the cloud platform resources is determined from the two dimensions of the resource data and the current running environment, so that the accuracy and the flexibility of resource adjustment are effectively improved, and the target cloud platform obtained after adjustment has better performance.

Fig. 3 schematically illustrates a flowchart of determining a cloud platform to be processed according to an embodiment of the disclosure.

As shown in fig. 3, the determination of the cloud platform to be processed in this embodiment includes operations S310 to S320.

In operation S310, cloud platform resource data is acquired and monitored in real time through a data lake.

In some embodiments, for example, the data lake may obtain the resource data from the cloud platform in real time through a monitoring service provided by the cloud platform, an application program interface call, an agent program, and the like, and monitor the cloud platform resource data in the data lake in real time.

In a specific implementation process, taking an application program interface (Application Program Interface, API) call as an example, acquiring cloud platform resource data in real time by using an API call mode includes: identity authentication is carried out based on an API key of the cloud platform; after the identity authentication is passed, sending a data acquisition request to the cloud platform through an API interface; and analyzing the API response and acquiring cloud platform resource data.

The API key is used as a credential for user authentication and authorization of the API request, and is typically transferred to the cloud platform as part of the data request, so that the cloud platform verifies the identity of the user and grants corresponding access rights based on the API key. Identity authentication is performed based on the API key, so that the security of cloud platform data can be effectively ensured, and unauthorized access and potential security risks are reduced.

After the identity verification is passed, a data acquisition request is constructed and sent to the cloud platform based on the format of the API interface, and the API response returned by the cloud platform is received and analyzed through the data lake, so that real-time acquisition and monitoring of cloud platform resources are realized. The cloud platform queries and acquires required resource data after receiving the data acquisition request, processes the resource data based on an API interface definition format, adds the processed resource data into an API response, sends the API response to a data lake, and analyzes the API response by the data lake and stores the resource data contained in the API response.

In operation S320, in the case that abnormal resource data is detected, the cloud platform corresponding to the resource data is determined as the cloud platform to be processed.

In some embodiments, the resource data may be monitored by setting a preset threshold to perform real-time monitoring of the anomaly data. Taking the memory use case as an example, setting a preset threshold value of the memory use case to 80%, and when the memory use case with the value exceeding 80% is monitored, judging the resource data as abnormal data and determining a cloud platform corresponding to the abnormal data as a cloud platform to be processed. The preset threshold value may be determined based on historical resource data, and the resource data value that may cause the cloud platform operation problem is determined by analyzing the historical resource data of the cloud platform, and the preset threshold value is set based on the resource data value. For example, when the memory usage is 88%, the cloud platform may run with high delay and Gao Kadu%, and the preset threshold may be adjusted based on the value, and the preset threshold is set to 85%, that is, when the memory usage is 85%, the cloud platform may be adjusted to prevent the possible problem of the cloud platform, and improve the management capability of the cloud platform.

Fig. 4 schematically illustrates a flowchart of acquiring a resource dataset of a cloud platform to be processed and a current operating environment of the cloud platform to be processed according to an embodiment of the disclosure.

As shown in fig. 4, the obtaining the resource data set of the cloud platform to be processed and the current running environment of the cloud platform to be processed in this embodiment includes operations S410 to S430.

In operation S410, a cloud platform to be processed is determined based on data information of the abnormal data.

In some embodiments, in response to monitoring the abnormal data, data information of the abnormal data is acquired to determine a cloud platform to which the abnormal data belongs, so that the cloud platform to which the abnormal data belongs is determined as a cloud platform to be processed. The data information is used to describe attributes of the resource data, and may include, for example, a name, a data type, a data format, a data size, a data creation time, a data owner, and the like of the resource data. The source and the generation mode of the abnormal data can be determined through the data information, so that the cloud platform to which the abnormal data belongs is determined.

In a specific implementation process, the data information can be automatically generated based on metadata management tools or data catalogue tools in the data lake, the tools can scan the resource data in the data lake and automatically generate corresponding data information according to the structure and the mode of the resource data, for example, in response to storing new resource data in the data lake, the metadata management tools are started to scan the newly-input resource data to obtain the data information of the newly-input resource data. In addition, the platform to which the abnormal data belongs may be determined by a data tracing mechanism, for example, the cloud platform to which the abnormal data belongs may be determined by tracing the source and/or flow path of the abnormal data.

In operation S420, all resource data of the cloud platform to be processed is queried in the data lake, and a resource data set is obtained.

In some embodiments, all resource data of the cloud platform to be processed is obtained by inquiring in a data lake based on platform information of the cloud platform to be processed, and a resource data set of the cloud platform to be processed is obtained.

In operation S430, a current operating environment is acquired from the cloud platform to be processed based on the query command.

In some embodiments, besides acquiring the resource data set from the data lake, the current running environment of the cloud platform needs to be acquired from the cloud platform to be processed, so that an adjustment strategy is formulated together from two aspects of the resource data and the current running environment, the adjustment strategy is more flexible, and the matching degree between the resource adjustment strategy and the cloud platform is improved. The current running environment of the cloud platform can be obtained through a query command. The query Command may be generated, for example, using a Command line interface (Command LINE INTERFACE, CLI) or a Software Development Kit (SDK) provided by the cloud platform to obtain the current running environment of the cloud platform to be processed through the query Command.

According to the embodiment of the invention, the cloud platform to be processed is quickly locked through the abnormal data, so that the timeliness of the resource adjustment of the cloud platform can be ensured, the problem cloud platform can be quickly positioned and optimized, and the continuous and stable operation of the cloud platform can be ensured. Furthermore, after the cloud platform to be processed is located, the embodiment of the invention also needs to acquire the information of two dimensions of the resource data set and the current running environment of the cloud platform to be processed, wherein the resource data set can reflect the use condition of each resource on the cloud platform to determine whether the resource expansion or release is needed. The current running environment can reflect the load condition, the user demand, the network condition and the like of the cloud platform, and the personalized condition of the cloud platform can be effectively reflected by acquiring the current running environment of the cloud platform, so that the adjustment strategy is optimized, and the flexibility and the accuracy of the resource adjustment of the cloud platform are improved.

Fig. 5 schematically illustrates a flowchart of determining an adjustment policy for a cloud platform to be processed according to an embodiment of the disclosure.

As shown in fig. 5, the determining the adjustment policy of the cloud platform to be processed in this embodiment includes operations S510 to S520.

In operation S510, a plurality of reports are obtained based on the resource data set, where the reports are used to reflect resource conditions of the cloud platform, and different reports correspond to different types of resource conditions.

In some embodiments, the cloud platform includes a plurality of resource types, such as a CPU, a memory, a storage, a server, a system disk, and the like, so that the plurality of resource data in the resource data set can be classified and calculated through the resource types, and the usage situation of each resource in the cloud platform is reflected through a plurality of reports, and report content can include, for example, the utilization rate, the usage idle rate, and the like of the resources, such as the CPU, the memory, the storage, and the like, so that the usage situation of each resource can be intuitively checked when the resource is adjusted, thereby quickly performing resource optimization.

In a specific implementation process, calculating a plurality of reports based on the resource data set includes: classifying data in the resource data set based on the resource type to obtain a plurality of data sets; respectively executing corresponding calculation operations on the data sets to obtain a plurality of reports; wherein the computing operations corresponding to the data sets of different resource types are different.

In operation S520, an adjustment policy of the cloud platform is determined based on the current environment of the cloud platform and the plurality of reports.

In some embodiments, the current operating environment of the cloud platform may include, for example, an operating system, network bandwidth, latency, etc., where the operating environment of the cloud platform may affect a resource usage manner, user expectations, etc. For example, in the case of limited network bandwidth, transmission of large amounts of data may be limited, resulting in wasted resources; or when the network delay of the region where the cloud platform is located is high, the requirement of the user in the current region on response time may be lower than that of the user in other regions. Therefore, the adjustment strategy is determined together through the current running environment of the cloud platform and the report, so that the accuracy of resource adjustment is improved, and the resource adjustment strategy is more in line with the current running environment and the user expectations.

Fig. 6 schematically illustrates a flowchart of determining an adjustment policy for a cloud platform to be processed according to an embodiment of the disclosure.

As shown in fig. 6, the determining the adjustment policy of the cloud platform to be processed in this embodiment includes operations S610 to S620.

In operation S610, a resource optimization direction is determined based on a current running environment of the cloud platform to be processed.

In operation S620, an adjustment policy is determined according to the report and the resource optimization direction.

In some embodiments, the current running environment of the cloud platform to be processed may have an impact on the utilization rate of resources and performance indexes, for example, the network bandwidth of the current running environment is limited, and applications for transmitting a large amount of data may be limited. In addition, the current operation environment can also reflect potential bottlenecks and problems of the cloud platform, for example, the problems such as network congestion, hardware faults, safety risks and the like which possibly exist can be found by analyzing the current operation environment of the cloud platform, and corresponding optimization measures can be timely taken.

In the specific implementation process, the adjustment positive strategy can be determined together according to the report and the resource optimization direction, for example, for the resource with the over-high utilization rate, the capacity of the resource can be increased by considering horizontal or vertical expansion; for resources with higher idle rate, releasing part of the resources or reallocating the resources can be considered. If the network bandwidth of the cloud platform to be processed is limited, the adjustment strategy can be further optimized by means of increasing the bandwidth capacity, optimizing the network topology structure and the like.

According to the embodiment of the disclosure, the adjustment strategy is jointly specified through the current running environment of the cloud platform and the resource data, so that the utilization rate of the resources can be effectively improved, the resources are reasonably distributed, and the utilization of the resources is improved. The performance bottleneck and the fault risk of the cloud platform can be timely found through detecting and analyzing the current running environment and the resource data of the cloud platform in real time, so that the cloud platform resource can be timely adjusted, the reliability of the cloud platform is improved, and the stable running of the service is ensured. And because the running environment of the cloud platform is changed in real time, the adjustment strategy is determined together based on the current running environment and the resource data, the dynamic adjustment of the resources can be realized, the elastic expansion and the reduction of the resources are realized, the change of different service loads and running environments is adapted, and the flexibility of the resource adjustment is effectively improved.

Fig. 7 schematically illustrates a flowchart of adjusting resources of a cloud platform to be processed based on an adjustment policy according to an embodiment of the disclosure.

As shown in fig. 7, the adjustment of the resource of the cloud platform to be processed based on the adjustment policy in this embodiment includes operations S710 to S720.

In operation S710, an instantiation association relationship between a virtual machine and a physical machine is acquired.

In some embodiments, instantiating the association refers to establishing a mapping between virtual machines and physical machines, i.e., determining which virtual machines are running on which physical machines. The relationship between the virtual machine and the physical machine can be clarified through instantiating the association relationship, so that the virtual machine can be associated with specific physical resources, the flexibility of resource adjustment is improved, resources in the cloud platform can be tracked and managed better, and meanwhile, the utilization condition of the resources can be estimated more accurately.

In operation S720, the physical machine resources and the virtual machine resources are adjusted based on the instantiation association relationship and the adjustment policy, respectively.

In some embodiments, physical machine resources and virtual machine resources may be co-tuned based on instantiation associations and optimization policies. By monitoring the instantiation association relationship and the resource utilization condition, corresponding optimization strategies such as dynamic migration of virtual machines, adjustment of resource allocation proportion and the like can be adopted, so that reasonable allocation and utilization of resources are realized, and the performance and efficiency of the whole system are improved.

In the implementation process, the following method can be adopted for adjusting the resources with low resource utilization rate: the resources are released by recycling the host which is offline or powered off and has no power-on plan; and recovering unused disks which are mounted on the cloud host and are mounted in the virtual machine, releasing storage resources and the like. The following ways can be adopted for the resources with higher resource utilization rate: for hosts with resource (CPU or memory) utilization rate exceeding 80%, performing hot migration adjustment on virtual machines on the hosts, and reducing the host resource utilization rate to below 80% so as to average the cloud host resource utilization rate. Resource optimization can also be performed according to a specific service scenario, for example, the following adjustment strategy can be adopted for a service with a single point host architecture: the redundancy of the host is realized by introducing the backup server or the cluster, and when the host fails, the host can be automatically switched to the backup server, so that the continuity of the service is ensured. Or the requests are distributed to a plurality of hosts through a load balancing technology, so that the load of each host is balanced, the pressure of a single host is reduced, and the performance and the usability of the whole system are improved. And an automatic fault transfer and recovery mechanism is configured, so that when the host fails, the service can be automatically transferred to other normal hosts, and the service can be timely recovered.

Based on the cloud platform resource adjustment method, the disclosure also provides a cloud platform resource adjustment device. The device will be described in detail below in connection with fig. 8.

Fig. 8 schematically illustrates a block diagram of a cloud platform resource adjustment device according to an embodiment of the present disclosure.

As shown in fig. 8, the cloud platform resource adjustment apparatus 800 of this embodiment includes a determination module 810, an acquisition module 820, and an adjustment module 830.

The determining module 810 is configured to determine, in response to the presence of the resource data anomaly, a cloud platform to be processed. In an embodiment, the determining module 810 may be configured to perform the operation S210 described above, which is not described herein.

The obtaining module 820 is configured to obtain a resource data set of the cloud platform to be processed and a current running environment of the cloud platform to be processed. In an embodiment, the obtaining module 820 may be configured to perform the operation S220 described above, which is not described herein.

The adjustment module 830 is configured to determine an adjustment policy of the cloud platform to be processed based on the resource data set and the current operating environment, and adjust resources of the cloud platform to be processed based on the adjustment policy, so as to obtain a target cloud platform. In an embodiment, the adjusting module 830 may be configured to perform the operation S230 described above, which is not described herein.

Any of the determining module 810, the obtaining module 820, and the adjusting module 830 may be combined in one module to be implemented, or any of the modules may be split into a plurality of modules according to an embodiment of the present disclosure. Or at least some of the functionality of one or more of the modules may be combined with, and implemented in, at least some of the functionality of other modules. According to embodiments of the present disclosure, at least one of the determination module 810, the acquisition module 820, and the adjustment module 830 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in hardware or firmware, such as any other reasonable manner of integrating or packaging the circuitry, or in any one of or a suitable combination of any of three implementations of software, hardware, and firmware. Or at least one of the determination module 810, the acquisition module 820 and the adjustment module 830 may be at least partially implemented as a computer program module which, when executed, may perform the corresponding functions.

Fig. 9 schematically illustrates a block diagram of an electronic device adapted to implement a cloud platform resource adjustment method according to an embodiment of the disclosure.

As shown in fig. 9, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage portion 908 into a Random Access Memory (RAM) 903. The processor 901 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 901 may also include on-board memory for caching purposes. Processor 901 may include a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the present disclosure.

In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other by a bus 904. The processor 901 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM 902 and/or the RAM 903. Note that the program may be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the disclosure, the electronic device 900 may also include an input/output (I/O) interface 905, the input/output (I/O) interface 905 also being connected to the bus 904. The electronic device 900 may also include one or more of the following components connected to the I/O interface 905: an input section 906 including a keyboard, a mouse, and the like; an output portion 907 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 908 including a hard disk or the like; and a communication section 909 including a network interface card such as a LAN card, a modem, or the like. The communication section 909 performs communication processing via a network such as the internet. The drive 910 is also connected to the I/O interface 905 as needed. A removable medium 911 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 910 so that a computer program read out therefrom is installed into the storage section 908 as needed.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, 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. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 902 and/or RAM 903 and/or one or more memories other than ROM 902 and RAM 903 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to implement the item recommendation method provided by embodiments of the present disclosure.

The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed, and downloaded and installed in the form of a signal on a network medium, via communication portion 909, and/or installed from removable medium 911. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909 and/or installed from the removable medium 911. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 901. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are described above. These examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims (12)

1. A cloud platform resource adjustment method, the method comprising:

determining a cloud platform to be processed in response to the presence of the resource data abnormality;

Acquiring a resource data set of the cloud platform to be processed and a current running environment of the cloud platform to be processed;

And determining an adjustment strategy of the cloud platform to be processed based on the resource data set and the current running environment, and adjusting the resources of the cloud platform to be processed based on the adjustment strategy to obtain a target cloud platform.

2. The cloud platform adjustment method according to claim 1, further comprising:

acquiring and monitoring cloud platform resource data in real time through a data lake;

And under the condition that abnormal resource data is monitored, determining the cloud platform corresponding to the resource data as a cloud platform to be processed.

3. The cloud platform resource adjustment method according to claim 2, wherein the acquiring and monitoring the cloud platform resource data in real time through the data lake comprises:

identity authentication is carried out based on the API key of the cloud platform;

after the identity authentication is passed, sending a data acquisition request to the cloud platform through an API (application program interface);

And analyzing an API response to acquire cloud platform resource data, wherein the API response is generated by the cloud platform based on the data acquisition request.

4. The cloud platform resource adjustment method according to claim 3, wherein the obtaining the resource dataset of the cloud platform to be processed and the current running environment of the cloud platform to be processed includes:

Determining a cloud platform to be processed based on the data information of the abnormal data;

Inquiring all resource data of the cloud platform to be processed in the data lake to obtain a resource data set; and

And acquiring the current running environment from the cloud platform to be processed based on the query command.

5. The cloud platform resource adjustment method according to claim 1, wherein the determining an adjustment policy of the cloud platform to be processed based on the resource data set and the current running environment comprises:

calculating a plurality of reports based on the resource data set, wherein the reports are used for reflecting resource conditions of a cloud platform, and different reports correspond to different types of resource conditions;

and determining an adjustment strategy of the cloud platform to be processed based on the current running environment of the cloud platform to be processed and the multiple reports.

6. The cloud platform resource adjustment method according to claim 5, wherein the calculating based on the resource data set to obtain a plurality of reports includes:

Classifying data in the resource data set based on the resource type to obtain a plurality of data sets; the resource type at least comprises one of a CPU, a memory, a storage, a server and a system disk;

respectively executing corresponding calculation operations on the data sets to obtain a plurality of reports; wherein the computing operations corresponding to the data sets of different resource types are different.

7. The cloud platform resource adjustment method of claim 5, wherein the determining an adjustment policy for the cloud platform based on the current environment of the cloud platform and the plurality of reports comprises:

Determining a resource optimization direction based on the current running environment of the cloud platform to be processed;

And determining an adjustment strategy according to the report and the resource optimization direction.

8. The method for adjusting resources of a cloud platform according to claim 7, wherein the adjusting resources of a cloud platform to be processed based on the adjustment policy to obtain a target cloud platform includes:

acquiring an instantiation association relationship between a virtual machine and a physical machine;

And respectively adjusting the physical machine resources and the virtual machine resources based on the instantiation association relation and the adjustment strategy.

9. A cloud platform resource adjustment device, the device comprising:

The determining module is used for determining a cloud platform to be processed in response to the presence of the abnormality of the resource data;

The acquisition module is used for acquiring a resource data set of the cloud platform to be processed and the current running environment of the cloud platform to be processed; and

And the adjustment module is used for determining an adjustment strategy of the cloud platform to be processed based on the resource data set and the current running environment, and adjusting the resources of the cloud platform to be processed based on the adjustment strategy to obtain a target cloud platform.

10. An electronic device, comprising:

One or more processors;

storage means for storing one or more computer programs,

Characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, realizes the steps of the method according to any one of claims 1-8.

12. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1-8.