Cloud resource management evaluation method and device, electronic equipment and storage medium

Abstract

The invention discloses a cloud resource management evaluation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining programming interface API running data generated by a user for cloud resource management; based on the API operation data, determining evaluation data used for describing the API use capability; obtaining index values of different dimensions according to the evaluation data; and determining an evaluation result of cloud resource management according to the index value of each dimension. The method comprises the following steps: acquiring cloud use data after a user is clouded, namely API operation data generated when the user manages cloud resources, so as to determine evaluation data used for describing the API use capability of the user, and providing a plurality of index values of different dimensions according to the evaluation data. Therefore, the index values of the dimensions are utilized to realize the assessment of the cloud resource automatic management level, so that the use rationality and use efficiency of the user on the resource management type API are measured, the user can carry out cloud service management in a targeted manner, and the cloud use automation capability of the user is improved.

Description

Technical Field

[0001] The present application relates to the field of cloud computing technology, and in particular to a cloud resource management evaluation method, device, electronic device and storage medium. Background Art

[0002] With the popularization of cloud computing technology, more and more individuals and enterprises have begun to use cloud computing services and put applications and data on the cloud. Cloud computing service providers provide various cloud resources for users to use, such as computing resources, storage resources, network resources, etc. In order to achieve efficient use of cloud resources, users can manage cloud resources. In the cloud, the management of cloud resources will directly affect the performance, availability and security of applications and data. Therefore, how to effectively evaluate the effect of user resource management in the cloud has become a technical problem that needs to be solved. Summary of the invention

[0003] The purpose of this application is to propose a cloud resource management evaluation method, device, electronic device and storage medium to address the deficiencies of the above-mentioned prior art, and this purpose is achieved through the following technical solutions.

[0004] The first aspect of the present application provides a cloud resource management evaluation method, the method comprising:

[0005] Obtain the API operation data generated by users for cloud resource management;

[0006] Determining evaluation data for describing API usage capability based on the API operation data;

[0007] Obtaining indicator values ​​of different dimensions according to the evaluation data;

[0008] The evaluation results of cloud resource management are determined based on the indicator values ​​of each dimension.

[0009] A second aspect of the present application provides a cloud resource management evaluation device, the device comprising:

[0010] The data acquisition module is used to obtain the program programming interface API operation data generated by the user for cloud resource management;

[0011] A data statistics module, used to determine evaluation data for describing API usage capabilities based on the API operation data;

[0012] A data evaluation module, used to obtain indicator values ​​of different dimensions based on the evaluation data;

[0013] The result determination module is used to determine the evaluation results of cloud resource management according to the indicator values ​​of each dimension.

[0014] The third aspect of the present application proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the first aspect above.

[0015] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method described in the first aspect above.

[0016] Based on the cloud resource management evaluation method and device described in the first and second aspects above, the present application has at least the following beneficial effects or advantages:

[0017] By obtaining the cloud usage data of users after they go to the cloud, that is, the API operation data generated when users manage cloud resources, we can determine the evaluation data used to describe the users' ability to use APIs, and provide indicator values ​​of multiple different dimensions based on these evaluation data. Then, we can use the indicator values ​​of these dimensions to evaluate the level of automated management of cloud resources, thereby measuring the rationality and efficiency of users' use of resource management APIs, facilitating users to conduct targeted cloud service governance, and improving users' automated ability to use the cloud.

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 The present invention is a flow chart of an embodiment of a cloud resource management evaluation method according to an exemplary embodiment;

[0021] Figure 2 This is a diagram showing an overall solution framework for implementing cloud resource management according to an exemplary embodiment;

[0022] Figure 3 It is a structural schematic diagram of a cloud resource management evaluation device according to an exemplary embodiment;

[0023] Figure 4 is a schematic diagram of a hardware structure of an electronic device according to an exemplary embodiment;

[0024] Figure 5The figure is a schematic diagram of the structure of a storage medium according to an exemplary embodiment. DETAILED DESCRIPTION

[0025] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0026] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0027] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0028] At present, in cloud governance, cloud resource supply and orchestration are the top priorities of cloud resource management. For example, for a multi-cloud strategy, enterprises need to consider a variety of cloud service providers to support different applications and workloads to achieve flexibility and elasticity in resource supply; service orchestration is the combination of different services by enterprises to support the deployment and operation of applications to achieve efficient resource orchestration; automated orchestration is the use of automation technology to improve resource utilization; containerization is the packaging of applications into containers by enterprises and running them in containers to achieve rapid deployment and migration of applications.

[0029] Considering the development of cloud resource management technology and the increase in demand, how to effectively evaluate the effectiveness of resource management has become a technical problem that needs to be solved.

[0030] In view of this, this application proposes a cloud resource management evaluation method to help enterprise users improve work efficiency, resource utilization, service quality, security, flexibility, etc., and make corresponding management decisions. It can also help cloud service providers better understand user needs and provide better services.

[0031] This evaluation method obtains the cloud usage data of users after they go to the cloud, that is, the API operation data generated when users manage cloud resources, to determine the evaluation data used to describe the user's ability to use the API, and provides indicator values ​​of multiple different dimensions based on these evaluation data. The indicator values ​​of these dimensions are then used to evaluate the level of automated management of cloud resources, thereby measuring the rationality and efficiency of users' use of resource management APIs, facilitating users to conduct targeted cloud service governance, and improving users' automated ability to use the cloud.

[0032] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The several specific embodiments listed can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0033] Figure 1 The flowchart of an embodiment of a cloud resource management evaluation method according to an exemplary embodiment includes the following steps 101 to 104:

[0034] Step 101: Obtain API operation data generated by the user for cloud resource management.

[0035] In this step, user management of cloud resources refers to the use of resource management APIs to manage cloud resources through various means. Specifically, the means used include but are not limited to consoles, command line tools, SDK (Software Development Kit), OpenAPI (Open Application Programming Interface), Terraform and other cloud governance tools to manage cloud resources. It should be noted here that among the cloud governance tools given above, except for the console provided by the cloud platform, other tools can be considered as automated tools for users to manage cloud resources in an integrated manner.

[0036] For example, assuming that the current user is enterprise A, when Zhang San, an administrator of enterprise A, uses a cloud governance tool to manage its own resources on the cloud, some API operation data will be generated. When it is necessary to conduct a cloud resource management assessment on enterprise A, Zhang San can send a cloud resource management assessment request to the cloud platform. After receiving the request, the cloud platform obtains the above API operation data.

[0037] In an embodiment of the present application, the acquired API operation data may include the original data of the API request initiated by the user and the original data of the API configuration.

[0038] Specifically, the original data of API requests records information such as the cloud governance tool used, request calling method, request time, number of requests, response time, error code, success code, etc.; the original data of API configuration (also known as API metadata) records the API name, version number, corresponding cloud product information, and various operating baselines (such as execution efficiency baseline, response success rate baseline, etc.).

[0039] Step 102: Determine evaluation data for describing API usage capability based on the API operation data.

[0040] In this step, since the API operation data can reflect the user's ability to use the API in all aspects, by analyzing the acquired API operation data, various evaluation data describing the API usage ability can be obtained, and these evaluation data are used to subsequently calculate various different dimensional indicators.

[0041] In an optional implementation, the following evaluation data may be determined based on the acquired API operation data:

[0042] 1. Total number of requests initiated by users: The total number of requests is obtained by counting the number of API requests contained in the API operation data. The total number of requests is the total number of requests for all APIs that the user can use, which can reflect the user's ability to call APIs by various means. Assuming that a user has the right to use two APIs, the user has initiated 6 requests using API1 and 10 requests using API2, then the total number of requests initiated by the user is 16.

[0043] 2. Total number of APIs used by users: By counting the different APIs included in the API operation data, the total number of APIs used by users can be obtained, which can reflect the user's ability to manage resources through APIs.

[0044] 3. The number of first requests initiated by users using automated tools: As mentioned earlier, in addition to the console provided by the cloud platform, integrated cloud governance tools (such as command line tools, SDK, OpenAPI, etc.) are all automated tools. Therefore, the number of times requests are initiated using non-consoles in the API operation data is counted to obtain the number of first requests. The number of first requests can reflect the user's ability to use the API in depth.

[0045] 4. The number of second requests when users use the preset calling method to call the API: It should be noted here that the preset calling method refers to a secure calling method, such as HTTPs (Hypertext Transfer Protocol Secure), which is a secure calling method. Therefore, the number of API requests using HTTPs in the API operation data can be counted to obtain the number of second requests. The number of second requests can reflect the user's ability to use the API safely and correctly.

[0046] 5. Number of APIs used by users through automation tools: By counting the number of APIs used through non-console in the API operation data, the number of APIs can reflect the user's automation capabilities in using APIs.

[0047] 6. Number of successful requests: The number of successful requests is obtained by counting the number of requests that return success codes in the API operation data. These requests indicate that all requests are successful, which can reflect the ability to use the API correctly.

[0048] 7. Response time of each API used by users: By obtaining the response time of each API request initiated by the user in the API operation data, and calculating the average response time of each API based on the response time of each API request, these averages can reflect the user's ability to use the API reasonably.

[0049] Based on the above description, the evaluation data determined according to the API operation data include: the total number of requests initiated by users, the total number of APIs used by users, the number of first requests initiated by users using automated tools, the number of second requests initiated by users using preset calling methods to call APIs, the number of APIs used by users through automated tools, the number of successful requests, and the response time of users using each API.

[0050] The above-mentioned evaluation data collected based on API operation data are only examples. Of course, the evaluation data can be further expanded according to actual needs to increase the dimension of describing the user's ability to use the API.

[0051] Step 103: Obtain indicator values ​​of different dimensions based on the evaluation data.

[0052] In this step, the index value of each dimension is used to characterize the degree of corresponding ability. The higher the index value, the stronger the corresponding ability.

[0053] For each evaluation data given in step 102, in a specific implementation, the following five-dimensional index values ​​can be obtained based on the evaluation data:

[0054] 1. API usage depth: Since the first request number reflects the user's ability to use the API deeply, in order to obtain the degree of this ability, specifically, the API usage depth can be represented by the ratio of the first request number to the total request number, thereby characterizing the user's depth of API usage.

[0055] 2. Depth of safe API usage: Since the second request number reflects the user's ability to use the API safely and correctly, in order to obtain the extent of this ability, the ratio of the second request number to the total request number can be used to represent the depth of safe API usage, thereby characterizing the user's degree of safe use of the API.

[0056] 3. Depth of API automation usage: Since the number of APIs used by users through automation tools can reflect the user's automation capabilities in using APIs, in order to obtain the extent of this capability, specifically, the ratio of the number of APIs to the total number of APIs used by users can be used to represent the depth of API automation usage, thereby characterizing the degree of user automation usage of APIs.

[0057] 4. API usage success rate: Since the number of successful requests reflects the ability to use the API correctly, in order to obtain the degree of this ability, specifically, the API usage success rate can be expressed as the ratio of the number of successful requests to the total number of requests, thereby characterizing the degree of correct use of the API by users.

[0058] 5. API execution efficiency: Since the API response time reflects the user's ability to reasonably use the API, in order to obtain the extent of this ability, the API execution efficiency can be determined by using the user's response time when using each API, thereby characterizing the user's reasonable use of the API.

[0059] Among them, since there are multiple APIs used by users, there is a response time for each API request, and the response time of each request is different, so it is necessary to comprehensively calculate the response time of each API request to calculate the API execution efficiency.

[0060] In one embodiment, for each API, the execution efficiency of the API can be determined according to the execution efficiency baseline and response time of the API, and then the average value of the execution efficiency of each API is taken as the total API execution efficiency.

[0061] Among them, the execution efficiency of a single API represents the reasonable degree of use of a single API by users. The overall API execution efficiency is obtained by combining the average value of the execution efficiency of each API.

[0062] The execution efficiency baseline of the API represents a reasonable time range for the API response time. Therefore, the execution efficiency baseline includes a lower limit value and an upper limit value. Here, the response time of the API refers to the average response time of the API.

[0063] When calculating the execution efficiency based on this execution efficiency baseline and the response time. Specifically, by comparing the response time of the API with the upper and lower limit values of the execution efficiency baseline, if the response time is greater than or equal to the upper limit value of the execution efficiency baseline, it indicates that the response time exceeds the upper limit of the reasonable time, and the execution efficiency of the API can be assigned a first preset value, which is used to represent a relatively low value of the execution efficiency of the API.

[0064] If the response time is between the lower limit value and the upper limit value of the execution efficiency baseline, the execution efficiency of the API can be determined based on the lower limit value, the upper limit value, and the response time. The specific calculation formula is O = W(N - N*(R - B) / B) / N + 1 / N), where W() represents the API execution efficiency calculation function, R represents the API response time, B represents the lower limit value, and N is a preset value representing the number of equal parts between the lower limit value and the upper limit value.

[0065] If the response time is less than or equal to the lower limit value, it indicates that the response time fully meets the reasonable time requirement, and the execution efficiency of the API can be assigned a second preset value, which represents a relatively high value of the execution efficiency of the API.

[0066] Here, assuming that the value range of the execution efficiency of the API is between 0 and 1, then the first preset value can be 0, and the second preset value can be 1. For the calculation process of the lower limit value and the upper limit value of the execution efficiency baseline of the API: the lower limit value B can be calculated based on the historical response times of all users using the API, and then the upper limit value A can be calculated based on the lower limit value B and the weighting coefficient M. The calculation formula for the upper limit value A is as follows:

[0067] When B <= 1s, A = B * 3M

[0068] When 1s < B <= 5s, A = B * 2M

[0069] When B > 5s, A = B * 1.5M

[0070] Among them, the 1 second and 5 seconds used for comparison with the lower limit value B in the above formula are both industry standard values for all APIs.

[0071] Step 104: Determine the evaluation result of cloud resource management based on the index values of each dimension.

[0072] In this step, the evaluation result represents the overall effect of cloud resource management.

[0073] In an optional embodiment, the management effectiveness score of cloud resource management is determined using the indicator values ​​of each dimension and the weights of each dimension, and then the automation management level to which the management effectiveness score belongs is determined based on preset automation management grading information, thereby generating an evaluation result using the management effectiveness score and the automation management level.

[0074] The automated management classification information includes the level classification information of different management effect scores. Assuming that the full score of the management effect score is 100, for example, the score level classification is: more than 80 points are healthy, more than 60 points are at risk, and less than 60 points are high risk. According to the above level classification standard, if the management effect score is 71 points, it means that the user's cloud resource automated management level is at a risk level.

[0075] Furthermore, the higher the management effect score, the stronger the user's ability to use API automation. The specific calculation formula for the management effect score is as follows:

[0076]

[0077] Where N represents the number of dimensions, ρ i represents the weight of the i-th dimension, S i Represents the index value of the i-th dimension.

[0078] It should be noted that after executing step 104, the evaluation results of cloud resource management can be displayed to the user, that is, the management effect score of the user's automated management and the automated management level to which it belongs. In addition, the indicator values ​​of each dimension can also be displayed, so that the user can govern the cloud resource service according to the displayed information to improve the corresponding indicator value, thereby improving the automated management effect.

[0079] For example, assuming that the user's API execution efficiency score on the cloud is 50 points, the corresponding weight is 0.6, and the weighted total score of other dimensions is 35 points, then the total management effect score is 35+50*0.6=65 points. After guiding the user to complete the governance of API execution efficiency based on this calculation result, assuming that the user's API execution efficiency score on the cloud becomes 100 points, then the total management effect score becomes 35+100*0.6=95 points. It can be seen that after governance, the management effect score increases by 95-65=35 points.

[0080] At this point, the above Figure 1The cloud resource management evaluation process shown in the figure obtains the cloud usage data of users after they go to the cloud, that is, the API operation data generated when the users manage cloud resources, to determine the evaluation data used to describe the users' ability to use the API, and provides indicator values ​​of multiple different dimensions based on these evaluation data. The indicator values ​​of these dimensions are then used to evaluate the level of automated management of cloud resources, thereby measuring the rationality and efficiency of users' use of resource management APIs, facilitating users to conduct targeted cloud service governance, and improving users' automated ability to use the cloud.

[0081] Based on the above Figure 1 Based on the illustrated embodiment, the evaluation process of cloud resource management is described in detail below. Figure 2 This is a diagram of an overall solution framework for implementing cloud resource management according to an exemplary embodiment. When a user uses a resource management API for cloud resource management, API operation data (including the original data of the API request and API metadata) will be generated. The data collection modules of five dimensions are used to perform statistics of different evaluation data based on the API operation data, including API usage module, API security call module, API automation usage module, API success rate module and API execution efficiency module. Among them, the API usage module is used to collect the number of times the user uses a non-console to initiate a request to obtain the first number of requests, and also collects the total number of requests initiated by the user; the API security call module is used to collect the number of second requests that the user uses to call the API using a preset calling method, that is, the number of requests to call the APP using HTTPs, and also collects the total number of requests initiated by the user; the API automation usage module is used to collect the number of APIs used by the user through the automation tool, and also collects the total number of APIs used by the user; the API success rate module is used to count the number of requests that return a success code to obtain the number of successful requests, and also collects the total number of requests initiated by the user; the API execution efficiency module is used to collect the response time of the user using each API, and also obtains the execution efficiency baseline of each API.

[0082] Then, each data collection module corresponds to a data calculation model, and the corresponding dimension indicator value is calculated by using the data calculation model. Specifically, the API usage model uses the first number of requests collected by the API usage module and the total number of requests to calculate the API usage depth; the API security usage model uses the second number of requests collected by the API security call module and the total number of requests to calculate the API security usage depth; the API automation depth model uses the number of APIs used by users through automation tools and the total number of APIs used by users collected by the API automation usage module to calculate the API automation usage depth; the API success rate model uses the number of successful requests and the total number of requests collected by the API success rate module to calculate the API usage success rate; the API execution efficiency model uses the response time of users using each API collected by the API execution efficiency module and the execution efficiency baseline of each API to calculate the API execution efficiency.

[0083] Then, an automated total score calculation model is used to perform weighted calculation on the indicator values ​​of each dimension obtained from the above calculation to obtain the total management effect score and determine the automated management level to which the management effect score belongs.

[0084] Finally, the final evaluation results are generated by combining the indicator values ​​of each dimension, management effect score, and automated management level obtained from the above calculations, and the evaluation results are displayed to users through the cloud governance center to guide users to adopt targeted governance solutions for cloud service governance (such as HTTPs governance, automated governance, exception governance, execution efficiency governance, etc.) to improve users' automation capabilities in using the cloud.

[0085] The execution subject of the embodiment of the present application may be an application, service, instance, software-based functional module, virtual machine (VM), container or cloud server, etc., or a hardware device (such as a server or terminal device) or hardware chip (such as CPU, GPU, FPGA, NPU, AI acceleration card or DPU) with data processing function. The device for implementing the evaluation of cloud resource management can be deployed on the computing device of the application party providing the corresponding service or on the cloud computing platform providing computing power, storage and network resources. The mode of providing services to the outside world by the cloud computing platform may be IaaS (Infrastructure as a Service), PaaS (Platform as a Service), SaaS (Software as a Service) or DaaS (Data as a Service). Taking the platform providing SaaS software as a service (Software as a Service) as an example, the cloud computing platform can use its own computing resources to provide the training of the evaluation model of cloud resource management or the functional execution of the evaluation module of cloud resource management, and the specific application architecture can be built according to the service requirements. For example, the platform can provide construction services based on the above model to applications or individuals using platform resources, and further call the above model and implement online or offline cloud resource management evaluation functions based on cloud resource management evaluation requests submitted by relevant clients or servers and other devices.

[0086] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0087] Corresponding to the aforementioned embodiment of the cloud resource management evaluation method, the present application also provides an embodiment of the cloud resource management evaluation device.

[0088] Figure 3 FIG. 1 is a schematic diagram of a cloud resource management evaluation device according to an exemplary embodiment. The device is used to execute the cloud resource management evaluation method provided in any of the above embodiments. Figure 3 As shown, the cloud resource management evaluation device includes:

[0089] The data acquisition module 310 is used to acquire the program programming interface API operation data generated by the user for cloud resource management;

[0090] A data statistics module 320, configured to determine evaluation data for describing API usage capabilities based on the API operation data;

[0091] A data evaluation module 330, used to obtain index values ​​of different dimensions according to the evaluation data;

[0092] The result determination module 340 is used to determine the evaluation result of cloud resource management according to the indicator values ​​of each dimension.

[0093] In an optional implementation, the data statistics module 320 is specifically used to determine, based on the API operation data, the total number of requests initiated by users, the total number of APIs used by users, the number of first requests initiated by users using automated tools, the number of second requests initiated by users using a preset calling method to call the API, the number of APIs used by users through automated tools, the number of successful requests, and the response time of users using each API.

[0094] In an optional implementation, the data evaluation module 330 is specifically used to determine the API usage depth using the first request number and the total request number; determine the API security usage depth using the second request number and the total request number; determine the API automation usage depth using the number of APIs and the total number of APIs used by users; determine the API usage success rate using the number of successful requests and the total number of requests; and determine the API execution efficiency using the response time of each API.

[0095] In an optional implementation, the data evaluation module 330 is specifically used to determine the execution efficiency of the API for each API based on the execution efficiency baseline and response time of the API in the process of determining the API execution efficiency by utilizing the response time of each API; and take the average value of the execution efficiency of each API as the total API execution efficiency.

[0096] In an optional implementation, the data evaluation module 330 is specifically used to, in the process of determining the execution efficiency of the API based on the execution efficiency baseline and response time of the API, assign the execution efficiency of the API to a first preset value when the response time is greater than or equal to the upper limit value of the execution efficiency baseline; determine the execution efficiency of the API based on the lower limit value, the upper limit value and the response time when the response time is between the lower limit value and the upper limit value of the execution efficiency baseline; and assign the execution efficiency of the API to a second preset value when the response time is less than or equal to the lower limit value; the second preset value is greater than the first preset value.

[0097] In an optional implementation, the device further includes ( Figure 3 Not shown):

[0098] The display module is used to display the evaluation result of cloud resource management to the user after the result determination module 340 determines the evaluation result of cloud resource management according to the indicator values ​​of each dimension.

[0099] In an optional implementation, the result determination module 340 is specifically used to determine the management effectiveness score of cloud resource management using the indicator values ​​of each dimension and the weights of each dimension; determine the automation management level to which the management effectiveness score belongs based on preset automation management grading information; and generate an evaluation result using the management effectiveness score and the automation management level.

[0100] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0101] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0102] The embodiments of the present application also provide an electronic device corresponding to the cloud resource management evaluation method provided in the aforementioned embodiments, so as to execute the aforementioned cloud resource management evaluation method.

[0103] Figure 4 6 is a hardware structure diagram of an electronic device according to an exemplary embodiment, and the electronic device includes: a communication interface 601, a processor 602, a memory 603 and a bus 604; wherein the communication interface 601, the processor 602 and the memory 603 complete mutual communication through the bus 604. The processor 602 can execute the cloud resource management evaluation method described above by reading and executing the machine executable instructions corresponding to the control logic of the cloud resource management evaluation method in the memory 603. The specific content of the method is referred to the above embodiment, and will not be repeated here.

[0104] The memory 603 mentioned in this application can be any electronic, magnetic, optical or other physical storage device, and can contain storage information, such as executable instructions, data, etc. Specifically, the memory 603 can be RAM (Random Access Memory), flash memory, storage drive (such as hard disk drive), any type of storage disk (such as optical disk, DVD, etc.), or similar storage medium, or a combination thereof. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 601 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0105] The bus 604 may be an ISA bus, a PCI bus or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 603 is used to store programs, and the processor 602 executes the programs after receiving execution instructions.

[0106] Processor 602 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in processor 602 or an instruction in software form. The above-mentioned processor 602 can be a general-purpose processor, including a network processor (Network Processor, referred to as NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as a hardware decoding processor to execute, or the hardware and software modules in the decoding processor are combined to execute.

[0107] The electronic device provided in the embodiment of the present application and the cloud resource management evaluation method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented therein.

[0108] The present application also provides a computer-readable storage medium corresponding to the cloud resource management evaluation method provided in the above embodiment. Figure 5 As shown, the computer-readable storage medium shown is a CD 30 on which a computer program (ie, a program product) is stored. When the computer program is executed by a processor, the cloud resource management evaluation method provided by any of the aforementioned embodiments will be executed.

[0109] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0110] The computer-readable storage medium provided by the above-mentioned embodiments of the present application and the cloud resource management evaluation method provided by the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0111] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0112] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0113] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A cloud resource management evaluation method, characterized in that: The method comprises: Obtain the API operation data generated by users for cloud resource management; Determining evaluation data for describing API usage capability based on the API operation data; Obtaining indicator values ​​of different dimensions according to the evaluation data; The evaluation results of cloud resource management are determined based on the indicator values ​​of each dimension.

2. The method according to claim 1, characterized in that The determining, based on the API operation data, of the evaluation data for describing the user's ability to use the API includes: Based on the API operation data, determine the total number of requests initiated by users, the total number of APIs used by users, the number of first requests initiated by users using automated tools, the number of second requests initiated by users using preset calling methods to call APIs, the number of APIs used by users through automated tools, the number of successful requests, and the response time of users using each API.

3. The method according to claim 2, characterized in that The obtaining of index values ​​of different dimensions according to the evaluation data includes: Determine the API usage depth using the first request number and the total request number; Determine the API security usage depth using the second request number and the total request number; Determine the depth of API automation usage using the number of APIs and the total number of APIs used by users; Determine the API usage success rate using the number of successful requests and the total number of requests; Use the response time of each API to determine the API execution efficiency.

4. The method according to claim 3, characterized in that Determining the API execution efficiency by using the response time of each API includes: For each API, determining the execution efficiency of the API according to the execution efficiency baseline and response time of the API; The average execution efficiency of each API is taken as the total API execution efficiency.

5. The method according to claim 4, characterized in that Determining the execution efficiency of the API according to the execution efficiency baseline and the response time of the API includes: When the response time is greater than or equal to the upper limit of the execution efficiency baseline, assigning the execution efficiency of the API to a first preset value; In a case where the response time is between a lower limit value and an upper limit value of the execution efficiency baseline, determining the execution efficiency of the API according to the lower limit value, the upper limit value and the response time; When the response time is less than or equal to the lower limit value, the execution efficiency of the API is assigned a second preset value; the second preset value is greater than the first preset value.

6. The method according to claim 1, characterized in that After determining the evaluation result of cloud resource management according to the indicator values ​​of each dimension, the method further includes: The evaluation result of the cloud resource management is presented to the user.

7. The method according to any one of claims 1 to 6, characterized in that: Determining the evaluation result of cloud resource management according to the indicator values ​​of each dimension includes: Determine the management effect score of cloud resource management by using the indicator values ​​and weights of each dimension; Determine the automation management level to which the management effect belongs according to preset automation management classification information; An evaluation result is generated using the management effectiveness score and the automated management level.

8. A cloud resource management evaluation device, characterized in that: The device comprises: The data acquisition module is used to obtain the program programming interface API operation data generated by the user for cloud resource management; A data statistics module, used to determine evaluation data for describing API usage capabilities based on the API operation data; A data evaluation module, used to obtain indicator values ​​of different dimensions based on the evaluation data; The result determination module is used to determine the evaluation results of cloud resource management according to the indicator values ​​of each dimension.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes the program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method according to any one of claims 1 to 7.

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