Cloud computing power management method and device, medium and program product

Abstract

The invention discloses a cloud computing power management method and device, a medium and a program product. The method relates to the technical field of cloud computing and artificial intelligence, and comprises the following steps: regularly collecting multi-dimensional computing power information corresponding to an application account, and identifying the computing power efficiency of each deployment node according to the multi-dimensional computing power information, so as to update a cloud computing power efficiency maintenance table of the application account according to the multi-dimensional computing power information and the computing power efficiency of each deployment node; when the cloud computing power efficiency maintenance table of the application account is updated, maintaining the to-be-governed list according to the computing power efficiency of each deployment node; and when the first time node is reached, issuing the to-be-governed list to the target computing power governing personnel and the target computing power user step by step. By adopting the technical scheme, full automation of a cloud computing power management process can be realized, low-efficiency deployment nodes are accurately positioned, manual subjective judgment errors are avoided, and the pertinence, effectiveness and traceability of management are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud computing and artificial intelligence, and particularly relates to a cloud computing power management method, device, medium and program product. BACKGROUND

[0002] With the continuous evolution of cloud computing technology, general computing power and intelligent computing power are widely used in enterprise digital transformation and become the core infrastructure supporting various application running, data processing and artificial intelligence model training and inference. In order to ensure the efficient operation of computing power resources, it is necessary to accurately identify inefficient scenarios in computing power use, promote the implementation of management measures and form a closed loop of continuous optimization.

[0003] At present, the operation and management of cloud computing power mainly adopts a traditional mode dominated by manual work. Relevant staff need to manually collect computing power data of different deployment nodes such as servers and containers, then form a list of inefficient computing power candidates by sorting and filtering, then hold a special meeting to explain the inefficient scenarios and management requirements to the relevant responsible persons, and clarify the management tasks and responsibility subjects, and rely on manual follow-up to promote the progress of management measures, and finally confirm the management results through manual feedback to complete a single management process.

[0004] The manual management mode of the prior art excessively relies on manual processing of the whole process, has low processing efficiency, and is prone to data omission and statistical errors due to human operation, which cannot meet the management needs of large-scale computing power resources, and the implementation progress and execution effect of the management measures are difficult to track in real time, and there is obvious process information difference. SUMMARY

[0005] The present application provides a cloud computing power management method, device, medium and program product, which can realize full automation of the cloud computing power management process, accurately locate inefficient deployment nodes, avoid subjective judgment errors of manual work, and effectively improve the pertinence, effectiveness and traceability of management.

[0006] According to an aspect of the present application, a cloud computing power management method is provided, comprising:

[0007] Collecting multi-dimensional computing power information corresponding to an application account at a regular time, and identifying the computing power efficiency of each deployment node according to the multi-dimensional computing power information, to update a cloud computing power efficiency maintenance table of the application account according to the multi-dimensional computing power information and the computing power efficiency of each deployment node;

[0008] Whenever the cloud computing power efficiency maintenance table of the application account is updated, maintaining a to-be-managed list according to the computing power efficiency of each deployment node;

[0009] When the first time node is reached, the to-be-governed list is issued to the target computing power governance personnel and the target computing power user in stages for the target computing power user to perform cloud computing power governance operations according to the to-be-governed list.

[0010] Optionally, the multi-dimensional computing power information corresponding to the application account is collected at a timing, and the computing power efficiency of each deployment node is identified according to the multi-dimensional computing power information, so as to update the cloud computing power efficiency maintenance table of the application account according to the multi-dimensional computing power information and the computing power efficiency of each deployment node, including:

[0011] The multi-item computing power key indicators of each deployment node corresponding to the application account are collected at a timing, and the low-efficiency identification model and the high-efficiency identification model of each deployment node are determined according to the application type of the application account;

[0012] The computing power efficiency of each deployment node is identified by using the low-efficiency identification model and the high-efficiency identification model respectively according to the multi-item computing power key indicators of the deployment node, and the multi-item computing power key indicators and the computing power efficiency of the deployment node are used to update the multiple table entries corresponding to the deployment node in the cloud computing power efficiency maintenance table of the application account.

[0013] The benefits of such a setting are that the exclusive identification model can be matched by the application type and the deployment node, avoiding the errors of manual subjective determination of low-efficiency nodes in the prior art, preventing misjudgment, and the cloud computing power efficiency maintenance table can standardize the data storage format, facilitating the subsequent automatic extraction and data tracing of the to-be-governed list, and solving the problem of chaotic list data format in the prior art.

[0014] Optionally, when the cloud computing power efficiency maintenance table of the application account is updated, the to-be-governed list is maintained according to the computing power efficiency of each deployment node, including:

[0015] When the cloud computing power efficiency maintenance table of the application account is updated, each target deployment node with low computing power efficiency is identified according to the current computing power efficiency of each deployment node in the cloud computing power efficiency maintenance table;

[0016] The to-be-governed list is updated according to the multiple table entries corresponding to each target deployment node in the cloud computing power efficiency maintenance table.

[0017] The benefits of such a setting are that the target deployment node is automatically screened, avoiding the omission problem of manually checking low-efficiency nodes one by one in the prior art, ensuring the completeness of the information of each to-be-governed node, and clarifying the basis for cloud computing power governance.

[0018] Optionally, when the first time node is reached, the to-be-governed list is issued to the target computing power governance personnel and the target computing power user in stages for the target computing power user to perform cloud computing power governance operations according to the to-be-governed list, including:

[0019] When the first time node is reached, a target computing power management personnel and a target computing power user corresponding to the application account are determined according to a current on-duty personnel situation;

[0020] The to-be-managed list is sent to the target computing power management personnel, and after detecting confirmation information fed back by the target computing power management personnel for the to-be-managed list, the to-be-managed list is sent to the target computing power user, so that the target computing power user performs a cloud computing power management operation according to the to-be-managed list.

[0021] The advantage of such a setting is that by issuing the process step by step, the auditing responsibility of the management personnel and the execution responsibility of the user can be clearly defined, the problem that no one is responsible after the task is issued in the prior art is solved, the list problem can be checked in advance through information confirmation by the target computing power management personnel, the incorrect list is avoided from being issued to the target computing power user, and invalid management operations are reduced.

[0022] Optionally, after the to-be-managed list is issued to the target computing power management personnel and the target computing power user step by step when the first time node is reached, the method further includes:

[0023] In response to an information registration request sent by the target computing power user, management measures of each target deployment node in the to-be-managed list are determined according to the to-be-managed list, and a management version plan of the application account is generated;

[0024] According to the management measures of each target deployment node and the management version plan of the application account, a management operation page is generated, and the management operation page is displayed to the target computing power user for viewing by the target computing power user.

[0025] The advantage of such a setting is that the risk of blind selection of management means by the user in the prior art can be avoided, the completion time and the person in charge can be recorded through the management version plan, the problem of no plan and uncontrollable progress in the prior art is solved, subsequent tracking is facilitated, all management information can be displayed through the management operation page, the user does not need to query scatteredly through emails or documents, and the operation efficiency is improved.

[0026] Optionally, after the to-be-managed list is issued to the target computing power management personnel and the target computing power user step by step when the first time node is reached, the method further includes:

[0027] If the management completion information fed back by the target computing power user is detected, the management completion information is forwarded to the computing power operation platform personnel for secondary confirmation by the computing power operation platform personnel, and after the secondary confirmation by the computing power operation platform personnel is completed, the computing power supply personnel is notified to feed back a management resource destination;

[0028] When the second time point is reached, the governance status is statistically analyzed based on the list of pending governance within the current governance cycle, the feedback information from the target computing power governance personnel, and the secondary confirmation information from the computing power operation platform personnel. Furthermore, the governance effect is analyzed based on the current operational status of each deployment node in the application account.

[0029] The advantages of this setup are as follows: the secondary confirmation process can prevent users from falsely reporting that governance has been completed, ensuring that governance measures are actually implemented; feedback from computing power providers on the whereabouts of resources can prevent the waste of idle resources after governance, improving the overall utilization rate of computing power; and through governance statistics and effect analysis, the monthly governance effectiveness can be effectively evaluated, providing data support for subsequent adjustments to governance strategies.

[0030] Optionally, after periodically collecting multi-dimensional computing power information corresponding to the application account, identifying the computing power performance of each deployment node based on the multi-dimensional computing power information, and updating the cloud computing power performance maintenance table of the application account based on the multi-dimensional computing power information and the computing power performance of each deployment node, the method further includes:

[0031] Whenever the cloud computing power performance maintenance table of an application account is updated, high-efficiency deployment nodes are identified based on the current computing power performance of each deployment node in the cloud computing power performance maintenance table.

[0032] Based on the multiple entries corresponding to each high-efficiency deployment node in the cloud computing power efficiency maintenance table, detect the expansion needs of high-efficiency deployment nodes, and update the list of nodes to be expanded when the target high-efficiency deployment node is detected to have expansion needs.

[0033] When the first time point is reached, the list of targets to be expanded will be distributed level by level to the target computing power governance personnel and the target computing power users, so that the target computing power users can perform expansion operations for the target efficient deployment nodes according to the list of targets to be expanded.

[0034] The advantages of this setup are that it reduces inefficient waste, meets the business needs of high-efficiency nodes, improves the matching degree of overall computing resources, supports automatic detection of expansion needs, and avoids the errors of manual judgment of expansion based on experience in the current technology.

[0035] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0036] At least one processor; and

[0037] A memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the cloud computing power governance method according to any embodiment of the present invention.

[0039] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the cloud computing power governance method according to any embodiment of the present invention.

[0040] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the cloud computing power governance method described in any embodiment of the present invention.

[0041] The technical solution of this invention collects multi-dimensional computing power information corresponding to application accounts at regular intervals, and identifies the computing power efficiency of each deployment node based on the multi-dimensional computing power information. It then updates the cloud computing power efficiency maintenance table of the application account based on the multi-dimensional computing power information and the computing power efficiency of each deployment node. Whenever the cloud computing power efficiency maintenance table of the application account is updated, a list of nodes to be governed is maintained based on the computing power efficiency of each deployment node. When the first time point is reached, the list of nodes to be governed is distributed level by level to the target computing power governance personnel and the target computing power users, allowing the target computing power users to perform cloud computing power governance operations based on the list. This method, driven by application accounts, collects and analyzes massive amounts of computing power information, thereby accurately identifying and locating inefficient deployment nodes of cloud computing power. This significantly reduces labor costs, avoids errors in subjective human judgment, and improves the efficiency and accuracy of identifying inefficient nodes. Furthermore, the list of nodes to be governed helps governance personnel quickly select the optimal solution, ensuring the professionalism of cloud computing power governance.

[0042] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of a cloud computing power management method provided in Embodiment 1 of the present invention;

[0045] Figure 2 This is a schematic diagram of a cloud computing power performance maintenance table provided according to an embodiment of the present invention;

[0046] Figure 3This is a flowchart of another cloud computing power management method provided in Embodiment 2 of the present invention;

[0047] Figure 4 This is a schematic diagram of the structure of a cloud computing power management device according to Embodiment 3 of the present invention;

[0048] Figure 5 This is a schematic diagram of the structure of an electronic device that implements the cloud computing power management method of this invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0051] Example 1

[0052] Figure 1 This is a flowchart of a cloud computing power governance method provided in Embodiment 1 of the present invention. This embodiment is applicable to the governance of inefficiently deployed nodes in application accounts. The method can be executed by a cloud computing power governance device, which can be implemented in hardware and / or software and is generally configured in a computer or processor with data processing capabilities. Figure 1 As shown, the method includes:

[0053] S110: Periodically collect multi-dimensional computing power information corresponding to the application account, and identify the computing power performance of each deployment node based on the multi-dimensional computing power information, so as to update the cloud computing power performance maintenance table of the application account according to the multi-dimensional computing power information and the computing power performance of each deployment node.

[0054] Optionally, multi-dimensional computing power information can refer to full-dimensional computing power data covering different deployment nodes with the application account as the core, which may include, but is not limited to, resource application information, resource usage information, deployment attribute information, etc. of servers, containers or graphics processors.

[0055] Optionally, a deployment node can refer to the specific carrier of cloud computing power. It can be divided into general computing power nodes and intelligent computing power nodes according to the type of computing power. General computing power nodes can include nodes such as infrastructure as a service servers, platform as a service containers, and general platform as a service databases. Intelligent computing power nodes can include graphics processor containers, graphics processor servers, etc. Deployment nodes can serve as the smallest operating unit for computing power performance identification and governance.

[0056] Optionally, a collection cycle can be set according to a preset period, such as daily or weekly, and driven by the application account to automatically aggregate multi-dimensional computing power information of all deployed nodes. Then, based on the computing power type and application type of the deployed node, the corresponding inefficient or efficient identification model is matched to determine the computing power performance of each deployed node. The computing power performance identification result can include inefficient, efficient or normal.

[0057] Optionally, the cloud computing power performance maintenance table can be used to record the resource ownership, computing power application status, computing power usage status, performance tags, application ownership, and other table items for all deployment nodes under each application account. In particular, when the deployment node is a graphics processor container, its corresponding table items can also include task name, service information, model type, task type, etc.

[0058] Optionally, the collected multi-dimensional computing power information and the determined performance tags can be synchronously updated to the cloud computing power performance maintenance table of the application account.

[0059] Figure 2 This is a schematic diagram of an optional cloud computing power performance maintenance table. Figure 2 This example uses only one deployment node for general-purpose computing power and one deployment node for intelligent computing power. Figure 2 As shown, the cloud computing power efficiency maintenance table records the resource ownership, computing power application status, computing power usage, inefficient label, and application ownership of each deployment node. Furthermore, for different deployment nodes, pre-set inefficient and efficient identification models are used. These models can determine whether each deployment node is inefficient based on the application type and the actual collected computing power information, and modify the inefficient label in the cloud computing power efficiency maintenance table accordingly. Figure 2 The table content is for illustrative purposes only. Figure 2 The purpose is to clarify the structure of the cloud computing power performance maintenance table, without limiting the content and format of the table.

[0060] This includes periodically collecting multi-dimensional computing power information corresponding to application accounts, identifying the computing power performance of each deployment node based on this information, and updating the cloud computing power performance maintenance table of the application account according to the multi-dimensional computing power information and the computing power performance of each deployment node. This may include:

[0061] Regularly collect multiple key computing power indicators of each deployment node corresponding to the application account, and determine the inefficient identification model and efficient identification model for each deployment node based on the application type of the application account.

[0062] Based on multiple key computing power indicators of the deployment nodes, the computing power performance of the deployment nodes is identified using both inefficient and efficient identification models. Then, using the multiple key computing power indicators and computing power performance of the deployment nodes, the corresponding entries in the cloud computing power performance maintenance table of the application account are updated.

[0063] Optionally, the key computing power indicators can refer to the computing power collection information that needs to be filled in for each deployment node in the cloud computing power efficiency maintenance table. Specifically, it can include indicators such as the daily average value, daily peak value and utilization rate of the central processing unit, resource ownership, template information, model type and task type. This is only an example.

[0064] Optionally, different application types can use different performance recognition thresholds in the inefficient recognition model and the efficient recognition model.

[0065] The advantages of this setup are: it can match a dedicated identification model with the application type and deployment node, avoiding the errors of manual subjective judgment of inefficient nodes in existing technologies, preventing misjudgments, and the cloud computing power performance maintenance table can standardize the data storage format, facilitating the automatic extraction and data traceability of the list to be governed, and solving the problem of chaotic data format in the manual organization of the list in existing technologies.

[0066] S120. Whenever the cloud computing power performance maintenance table of the application account is updated, maintain the list of entities to be governed based on the computing power performance of each deployment node.

[0067] Optionally, whenever the performance maintenance table is updated, deployment nodes with the performance tag "inefficient" can be automatically filtered out and identified as target deployment nodes. Key information of the target deployment nodes can then be extracted and integrated into a list to be addressed.

[0068] Whenever the cloud computing power performance maintenance table of an application account is updated, a list of entities to be addressed is maintained based on the computing power performance of each deployment node. This list may include:

[0069] Whenever the cloud computing power performance maintenance table of the application account is updated, the target deployment nodes with low computing power performance are identified based on the current computing power performance of each deployment node in the cloud computing power performance maintenance table.

[0070] Update the list of entities to be addressed based on the multiple entries corresponding to each target deployment node in the cloud computing power performance maintenance table.

[0071] Optionally, for each target deployment node, the corresponding core table entry information can be extracted from the performance maintenance table. This information may include node type, resource ownership, reason for inefficiency, requested computing power specifications, associated application account, application owner, etc., and this information can be integrated in a standardized format to update the list to be addressed.

[0072] The advantage of this setup is that by automatically selecting target deployment nodes, it avoids the problem of missing inefficient nodes by manually checking them one by one, ensuring the completeness of information for each node to be governed and clarifying the basis for cloud computing power governance.

[0073] S130. When the first time point is reached, the list of tasks to be governed will be distributed level by level to the target computing power governance personnel and the target computing power users, so that the target computing power users can perform cloud computing power governance operations according to the list of tasks to be governed.

[0074] Optionally, the first time node can refer to a preset fixed task distribution time, the target computing power governance personnel are the personnel responsible for coordinating computing power governance on the application side, the target computing power user are the technical implementation personnel who directly use computing power, and the computing power governance personnel and computing power users are bound to the application account to clarify the governance responsibility chain.

[0075] Optionally, when the first time point is reached, the on-duty status of the personnel maintained on the platform side can be called to determine the target computing power governance personnel and target computing power users corresponding to the application account. The list to be governed is first sent to the target computing power governance personnel. After their confirmation, it is then forwarded to the target computing power users, who then perform governance operations according to the list.

[0076] When the first time point is reached, the list of entities to be governed will be distributed level by level to the target computing power governance personnel and the target computing power users, so that the target computing power users can perform cloud computing power governance operations based on the list, which may include:

[0077] Upon reaching the first time point, based on the current on-duty status of maintenance personnel, determine the target computing power governance personnel and target computing power users corresponding to the application account;

[0078] The list of entities to be governed is sent to the target computing power governance personnel. Once the confirmation information from the target computing power governance personnel regarding the list of entities to be governed is detected, the list of entities to be governed is sent to the target computing power users so that the target computing power users can perform cloud computing power governance operations based on the list of entities to be governed.

[0079] Optionally, the list of entities to be governed can be sent to the email address of the target computing power governance personnel, along with a confirmation entry. After viewing the list, the target computing power governance personnel can click the entry to provide confirmation information. The confirmation information may include confirmation instructions such as "received" or "adjustment required," which is used to trigger the forwarding process of the list to the target computing power users, ensuring that the governance personnel are aware of and approve of the list content.

[0080] Optionally, after detecting the confirmation information from the target computing power governance personnel, the list of entities to be governed is automatically forwarded to the email address of the target computing power user, who then formulates and executes a governance plan based on the list.

[0081] The advantages of this setup are: by distributing the process step by step, the review responsibilities of governance personnel and the execution responsibilities of users can be clearly defined, solving the problem of no one being responsible after the existing technical tasks are distributed. By having the target computing power governance personnel confirm the information, problems with the list can be identified in advance, avoiding the distribution of incorrect lists to the target computing power users and reducing ineffective governance operations.

[0082] This includes, after the list of entities to be governed is distributed level by level to the target computing power governance personnel and the target computing power users when the first time point is reached, the following may also be included:

[0083] In response to the information registration request sent by the target computing power user, based on the list of targets to be governed, the governance measures for each target deployment node in the list of targets to be governed are determined, and a governance version plan for the application account is generated;

[0084] Based on the governance measures of each target deployment node and the governance version plan of the application account, a governance operation page is generated and displayed to the target computing power users for their viewing.

[0085] Optionally, the information registration request is a request initiated by the target computing power user to the system after receiving the list to be governed, by clicking the governance registration link in the email, which is used to trigger the process of entering governance measures and formulating plans.

[0086] Optionally, governance measures are specific optimization methods for inefficient nodes. Optimization methods for different situations can be pre-maintained through a technical measures library. These optimization methods may include, but are not limited to, scaling down, container co-location, AI storage acceleration, GPU shared co-location, offline or database removal, etc. The technical measures library can maintain specific governance categories for each governance technology, as well as the technical principles and applicable scenarios corresponding to each governance category.

[0087] Furthermore, based on the information of each deployment node in the list to be governed, and by comparing it with the applicable scenarios corresponding to each governance category in the technical measures library, the governance category that can be used to govern the target deployment node can be identified. Then, based on the specific technical principles corresponding to that governance category, the governance measures for the target deployment node can be determined.

[0088] Optionally, the governance version plan can refer to the governance timeline developed by the user, which specifies the governance completion time, responsible person, key nodes, and other information for each target deployment node to ensure the orderly progress of governance.

[0089] Optionally, the governance and operation page can integrate information such as a list of nodes to be governed, governance measures for each node, governance version plan, person filling in the information, and update time, allowing users to view, edit, and track the governance progress.

[0090] The advantages of this setup are: it avoids the risk of users of existing technologies blindly choosing governance methods; the governance version plan can record the completion time and responsible person, solving the problems of unplanned governance and uncontrollable progress in existing technologies, facilitating subsequent tracking; and it can centrally display all governance information through the governance operation page, eliminating the need for users to search piecemeal through emails or documents, thus improving operational efficiency.

[0091] This includes, after the list of entities to be governed is distributed level by level to the target computing power governance personnel and the target computing power users when the first time point is reached, the following may also be included:

[0092] If the governance completion information is detected from the target computing power user, the governance completion information will be forwarded to the computing power operation platform personnel for secondary confirmation. After the computing power operation platform personnel complete the secondary confirmation, the computing power supply personnel will be notified to provide feedback on the destination of the governance resources.

[0093] When the second time point is reached, the governance status is statistically analyzed based on the list of pending governance within the current governance cycle, the feedback information from the target computing power governance personnel, and the secondary confirmation information from the computing power operation platform personnel. Furthermore, the governance effect is analyzed based on the current operational status of each deployment node in the application account.

[0094] Optionally, governance completion information can refer to the operation completion instructions fed back to the system by the target computing power user after completing the governance operation, which may include governance node, completion time, operation content, etc.

[0095] Optionally, the personnel of the computing power operation platform can verify the authenticity and effectiveness of the governance completion information to ensure that the governance operation is actually implemented and avoid false feedback.

[0096] Optionally, if the computing power operation platform personnel confirm the approval a second time, the computing power supply personnel will be notified to check the resource status of the governance node. The computing power supply personnel can refer to the personnel on the platform side responsible for the allocation and scheduling of computing power resources. The computing power supply personnel can provide feedback on the destination of the resources after governance, such as reusing them for other applications, returning them from the database, or including them in the public resource pool, to ensure efficient reuse of resources.

[0097] Optionally, the second time node can refer to a preset time for statistical analysis of governance effects, such as the last working day of each month. At the second time node, information such as the total number of nodes to be governed, the number of nodes that have been governed, the number of nodes that have been overdue and not yet completed, and the governance completion rate can be statistically analyzed to achieve governance status statistics. Through governance effect analysis, the changes in computing power utilization after governance can be analyzed to evaluate the effect of governance on improving overall computing power efficiency.

[0098] Optionally, the governance effect analysis may include comparing key computing power indicators before and after governance, analyzing the impact of reducing inefficient nodes on overall computing power performance, and generating a comparison view of objectives and results.

[0099] The advantages of this setup are as follows: the secondary confirmation process can prevent users from falsely reporting that governance has been completed, ensuring that governance measures are actually implemented; feedback from computing power providers on the whereabouts of resources can prevent the waste of idle resources after governance, improving the overall utilization rate of computing power; and through governance statistics and effect analysis, the monthly governance effectiveness can be effectively evaluated, providing data support for subsequent adjustments to governance strategies.

[0100] This process, which involves periodically collecting multi-dimensional computing power information corresponding to application accounts and identifying the computing power performance of each deployment node based on this information, and then updating the cloud computing power performance maintenance table of the application account according to the multi-dimensional computing power information and the computing power performance of each deployment node, may further include:

[0101] Whenever the cloud computing power performance maintenance table of an application account is updated, high-efficiency deployment nodes are identified based on the current computing power performance of each deployment node in the cloud computing power performance maintenance table.

[0102] Based on the multiple entries corresponding to each high-efficiency deployment node in the cloud computing power efficiency maintenance table, detect the expansion needs of high-efficiency deployment nodes, and update the list of nodes to be expanded when the target high-efficiency deployment node is detected to have expansion needs.

[0103] When the first time point is reached, the list of targets to be expanded will be distributed level by level to the target computing power governance personnel and the target computing power users, so that the target computing power users can perform expansion operations for the target efficient deployment nodes according to the list of targets to be expanded.

[0104] Optionally, a high-efficiency deployment node can refer to a deployment node with the efficiency label set to high in the cloud computing power efficiency maintenance table, that is, a node with high resource utilization that needs further expansion to support business needs.

[0105] Optionally, the necessity of expanding the capacity of high-efficiency deployment nodes can be determined based on the key computing power indicators of high-efficiency deployment nodes. The determination logic includes: computing power utilization rate continuously ≥90% in the past month, service call volume increasing by ≥50% month-on-month, or the difference between peak computing power and computing power request volume is less than 10%.

[0106] Optionally, the list of nodes to be expanded can refer to a standardized list that integrates information on efficient deployment nodes, which may include information such as node type, application, current computing power specifications, reasons for expansion needs, and suggested expansion specifications.

[0107] Optionally, when the first time point is reached, the list can be sent to the target computing power governance personnel for confirmation based on the hierarchical distribution mechanism, and then forwarded to the target computing power users, who can then perform the expansion operation according to the list.

[0108] The advantages of this setup are that it reduces inefficient waste, meets the business needs of high-efficiency nodes, improves the matching degree of overall computing resources, supports automatic detection of expansion needs, and avoids the errors of manual judgment of expansion based on experience in the current technology.

[0109] The technical solution of this invention collects multi-dimensional computing power information corresponding to application accounts at regular intervals, and identifies the computing power efficiency of each deployment node based on the multi-dimensional computing power information. It then updates the cloud computing power efficiency maintenance table of the application account based on the multi-dimensional computing power information and the computing power efficiency of each deployment node. Whenever the cloud computing power efficiency maintenance table of the application account is updated, a list of nodes to be governed is maintained based on the computing power efficiency of each deployment node. When the first time point is reached, the list of nodes to be governed is distributed level by level to the target computing power governance personnel and the target computing power users, allowing the target computing power users to perform cloud computing power governance operations based on the list. This method, driven by application accounts, collects and analyzes massive amounts of computing power information, thereby accurately identifying and locating inefficient deployment nodes of cloud computing power. This significantly reduces labor costs, avoids errors in subjective human judgment, and improves the efficiency and accuracy of identifying inefficient nodes. Furthermore, the list of nodes to be governed helps governance personnel quickly select the optimal solution, ensuring the professionalism of cloud computing power governance.

[0110] Example 2

[0111] Figure 3 This is a flowchart illustrating a cloud computing power governance method according to Embodiment 2 of the present invention. Based on the above embodiments, this embodiment specifically describes the cloud computing power governance method. Figure 3 As shown, the method includes:

[0112] S210: Periodically collect multiple key computing power indicators of each deployment node corresponding to the application account, and determine the inefficient identification model and efficient identification model of each deployment node according to the application type of the application account.

[0113] S220. Based on multiple key computing power indicators of the deployment node, identify the computing power performance of the deployment node using both inefficient and efficient identification models. Then, using the multiple key computing power indicators and computing power performance of the deployment node, update multiple entries corresponding to the deployment node in the cloud computing power performance maintenance table of the application account.

[0114] S230. Whenever the cloud computing power performance maintenance table of the application account is updated, identify each target deployment node with low computing power performance based on the current computing power performance of each deployment node in the cloud computing power performance maintenance table.

[0115] S240. Update the list of entities to be addressed based on the multiple entries corresponding to each target deployment node in the cloud computing power performance maintenance table.

[0116] S250. When the first time node is reached, determine the target computing power governance personnel and target computing power users corresponding to the application account based on the on-duty status of the current maintenance personnel.

[0117] S260. Send the list of tasks to be governed to the target computing power governance personnel, and after detecting the confirmation information from the target computing power governance personnel regarding the list of tasks to be governed, send the list of tasks to be governed to the target computing power user so that the target computing power user can perform cloud computing power governance operations according to the list of tasks to be governed.

[0118] S270. If governance completion information is detected from the target computing power user, the governance completion information is forwarded to the computing power operation platform personnel for secondary confirmation. After the computing power operation platform personnel complete the secondary confirmation, the computing power supply personnel are notified to provide feedback on the destination of the governance resources.

[0119] S280. When the second time node is reached, the governance status is statistically analyzed based on the list of pending governance within the current governance cycle, the feedback information from the target computing power governance personnel, and the secondary confirmation information from the computing power operation platform personnel. The governance effect is also analyzed based on the current operating status of each deployment node in the application account.

[0120] The technical solution of this invention collects multi-dimensional computing power information corresponding to application accounts at regular intervals, and identifies the computing power efficiency of each deployment node based on the multi-dimensional computing power information. It then updates the cloud computing power efficiency maintenance table of the application account based on the multi-dimensional computing power information and the computing power efficiency of each deployment node. Whenever the cloud computing power efficiency maintenance table of the application account is updated, a list of nodes to be governed is maintained based on the computing power efficiency of each deployment node. When the first time point is reached, the list of nodes to be governed is distributed level by level to the target computing power governance personnel and the target computing power users, allowing the target computing power users to perform cloud computing power governance operations based on the list. This method, driven by application accounts, collects and analyzes massive amounts of computing power information, thereby accurately identifying and locating inefficient deployment nodes of cloud computing power. This significantly reduces labor costs, avoids errors in subjective human judgment, and improves the efficiency and accuracy of identifying inefficient nodes. Furthermore, the list of nodes to be governed helps governance personnel quickly select the optimal solution, ensuring the professionalism of cloud computing power governance.

[0121] Example 3

[0122] Figure 4 This is a schematic diagram of a cloud computing power management device provided in Embodiment 3 of the present invention. Figure 4 As shown, the device includes: a cloud computing power performance maintenance table update module 310, a list of items to be managed maintenance module 320, and a list of items to be managed distribution module 330.

[0123] The cloud computing power performance maintenance table update module 310 is used to periodically collect multi-dimensional computing power information corresponding to the application account, and identify the computing power performance of each deployment node based on the multi-dimensional computing power information, so as to update the cloud computing power performance maintenance table of the application account according to the multi-dimensional computing power information and the computing power performance of each deployment node.

[0124] The pending governance list maintenance module 320 is used to maintain the pending governance list based on the computing power performance of each deployment node whenever the cloud computing power performance maintenance table of the application account is updated.

[0125] The module 330, which distributes the list of pending governance, is used to distribute the list of pending governance level by level to the target computing power governance personnel and the target computing power users when the first time node is reached, so that the target computing power users can perform cloud computing power governance operations according to the list of pending governance.

[0126] The technical solution of this invention collects multi-dimensional computing power information corresponding to application accounts at regular intervals, and identifies the computing power efficiency of each deployment node based on the multi-dimensional computing power information. It then updates the cloud computing power efficiency maintenance table of the application account based on the multi-dimensional computing power information and the computing power efficiency of each deployment node. Whenever the cloud computing power efficiency maintenance table of the application account is updated, a list of nodes to be governed is maintained based on the computing power efficiency of each deployment node. When the first time point is reached, the list of nodes to be governed is distributed level by level to the target computing power governance personnel and the target computing power users, allowing the target computing power users to perform cloud computing power governance operations based on the list. This method, driven by application accounts, collects and analyzes massive amounts of computing power information, thereby accurately identifying and locating inefficient deployment nodes of cloud computing power. This significantly reduces labor costs, avoids errors in subjective human judgment, and improves the efficiency and accuracy of identifying inefficient nodes. Furthermore, the list of nodes to be governed helps governance personnel quickly select the optimal solution, ensuring the professionalism of cloud computing power governance.

[0127] Based on the above embodiments, the cloud computing power performance maintenance table update module 310 can be specifically used for:

[0128] Regularly collect multiple key computing power indicators of each deployment node corresponding to the application account, and determine the inefficient identification model and efficient identification model for each deployment node based on the application type of the application account.

[0129] Based on multiple key computing power indicators of the deployment nodes, the computing power performance of the deployment nodes is identified using both inefficient and efficient identification models. Then, using the multiple key computing power indicators and computing power performance of the deployment nodes, the corresponding entries in the cloud computing power performance maintenance table of the application account are updated.

[0130] Based on the above embodiments, the to-be-managed list maintenance module 320 can be specifically used for:

[0131] Whenever the cloud computing power performance maintenance table of the application account is updated, the target deployment nodes with low computing power performance are identified based on the current computing power performance of each deployment node in the cloud computing power performance maintenance table.

[0132] Update the list of entities to be addressed based on the multiple entries corresponding to each target deployment node in the cloud computing power performance maintenance table.

[0133] Based on the above embodiments, the governance list distribution module 330 can be specifically used for:

[0134] Upon reaching the first time point, based on the current on-duty status of maintenance personnel, determine the target computing power governance personnel and target computing power users corresponding to the application account;

[0135] The list of entities to be governed is sent to the target computing power governance personnel. Once the confirmation information from the target computing power governance personnel regarding the list of entities to be governed is detected, the list of entities to be governed is sent to the target computing power users so that the target computing power users can perform cloud computing power governance operations based on the list of entities to be governed.

[0136] Based on the above embodiments, a governance and operation page generation module may also be included, used for:

[0137] In response to the information registration request sent by the target computing power user, based on the list of targets to be governed, the governance measures for each target deployment node in the list of targets to be governed are determined, and a governance version plan for the application account is generated;

[0138] Based on the governance measures of each target deployment node and the governance version plan of the application account, a governance operation page is generated and displayed to the target computing power users for their viewing.

[0139] Based on the above embodiments, a governance monitoring module may also be included, for:

[0140] If the governance completion information is detected from the target computing power user, the governance completion information will be forwarded to the computing power operation platform personnel for secondary confirmation. After the computing power operation platform personnel complete the secondary confirmation, the computing power supply personnel will be notified to provide feedback on the destination of the governance resources.

[0141] When the second time point is reached, the governance status is statistically analyzed based on the list of pending governance within the current governance cycle, the feedback information from the target computing power governance personnel, and the secondary confirmation information from the computing power operation platform personnel. Furthermore, the governance effect is analyzed based on the current operational status of each deployment node in the application account.

[0142] Based on the above embodiments, a high-efficiency deployment node expansion module may also be included, for:

[0143] Whenever the cloud computing power performance maintenance table of an application account is updated, high-efficiency deployment nodes are identified based on the current computing power performance of each deployment node in the cloud computing power performance maintenance table.

[0144] Based on the multiple entries corresponding to each high-efficiency deployment node in the cloud computing power efficiency maintenance table, detect the expansion needs of high-efficiency deployment nodes, and update the list of nodes to be expanded when the target high-efficiency deployment node is detected to have expansion needs.

[0145] When the first time point is reached, the list of targets to be expanded will be distributed level by level to the target computing power governance personnel and the target computing power users, so that the target computing power users can perform expansion operations for the target efficient deployment nodes according to the list of targets to be expanded.

[0146] The cloud computing power management device provided in the embodiments of the present invention can execute the cloud computing power management method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0147] Example 4

[0148] Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0149] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0150] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0151] Processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the cloud computing power governance method described in the embodiments of the present invention. That is:

[0152] Periodically collect multi-dimensional computing power information corresponding to application accounts, and identify the computing power performance of each deployment node based on the multi-dimensional computing power information, so as to update the cloud computing power performance maintenance table of application accounts according to the multi-dimensional computing power information and the computing power performance of each deployment node.

[0153] Whenever the cloud computing power performance maintenance table of the application account is updated, a list of issues to be addressed is maintained based on the computing power performance of each deployment node.

[0154] When the first time point is reached, the list of entities to be governed will be distributed level by level to the target computing power governance personnel and the target computing power users, so that the target computing power users can perform cloud computing power governance operations according to the list of entities to be governed.

[0155] In some embodiments, the cloud computing power governance method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the cloud computing power governance method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the cloud computing power governance method by any other suitable means (e.g., by means of firmware).

[0156] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0158] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0159] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0160] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0161] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0162] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0163] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A cloud computing power governance method, characterized in that, include: Periodically collect multi-dimensional computing power information corresponding to application accounts, and identify the computing power performance of each deployment node based on the multi-dimensional computing power information, so as to update the cloud computing power performance maintenance table of application accounts according to the multi-dimensional computing power information and the computing power performance of each deployment node. Whenever the cloud computing power performance maintenance table of the application account is updated, a list of issues to be addressed is maintained based on the computing power performance of each deployment node. When the first time point is reached, the list of entities to be governed will be distributed level by level to the target computing power governance personnel and the target computing power users, so that the target computing power users can perform cloud computing power governance operations according to the list of entities to be governed.

2. The method of claim 1, wherein, Periodically collect multi-dimensional computing power information corresponding to application accounts, and identify the computing power performance of each deployment node based on this information. Update the cloud computing power performance maintenance table for application accounts based on the multi-dimensional computing power information and the computing power performance of each deployment node, including: Regularly collect multiple key computing power indicators of each deployment node corresponding to the application account, and determine the inefficient identification model and efficient identification model for each deployment node based on the application type of the application account. Based on multiple key computing power indicators of the deployment nodes, the computing power performance of the deployment nodes is identified using both inefficient and efficient identification models. Then, using the multiple key computing power indicators and computing power performance of the deployment nodes, the corresponding entries in the cloud computing power performance maintenance table of the application account are updated.

3. The method of claim 1, wherein, Whenever the cloud computing power performance maintenance table for an application account is updated, a list of items to be addressed is maintained based on the computing power performance of each deployment node, including: Whenever the cloud computing power performance maintenance table of the application account is updated, the target deployment nodes with low computing power performance are identified based on the current computing power performance of each deployment node in the cloud computing power performance maintenance table. Update the list of entities to be addressed based on the multiple entries corresponding to each target deployment node in the cloud computing power performance maintenance table.

4. The method of claim 1, wherein, Upon reaching the first time point, the list of entities to be governed will be distributed level by level to the target computing power governance personnel and the target computing power users, so that the target computing power users can perform cloud computing power governance operations based on the list, including: Upon reaching the first time point, based on the current on-duty status of maintenance personnel, determine the target computing power governance personnel and target computing power users corresponding to the application account; The list of entities to be governed is sent to the target computing power governance personnel. Once the confirmation information from the target computing power governance personnel regarding the list of entities to be governed is detected, the list of entities to be governed is sent to the target computing power users so that the target computing power users can perform cloud computing power governance operations based on the list of entities to be governed.

5. The method of claim 1, wherein, Upon reaching the first time point, after distributing the list of entities to be governed level by level to the target computing power governance personnel and the target computing power users, it also includes: In response to the information registration request sent by the target computing power user, based on the list of targets to be governed, the governance measures for each target deployment node in the list of targets to be governed are determined, and a governance version plan for the application account is generated; Based on the governance measures of each target deployment node and the governance version plan of the application account, a governance operation page is generated and displayed to the target computing power users for their viewing.

6. The method of claim 1, wherein, After the to-be-governed list is issued to the target computing power management personnel and the target computing power user at the first time node, the method further includes: If the management completion information fed back by the target computing power user is detected, the management completion information is forwarded to the computing power operation platform personnel for secondary confirmation, and after the secondary confirmation of the computing power operation platform personnel is completed, the computing power supplier is notified to feed back the management resource destination; When the second time node is reached, the management situation is counted according to the to-be-governed list in the current management period, the feedback information of the target computing power management personnel and the secondary confirmation information of the computing power operation platform personnel, and the management effect is analyzed according to the current operation state of each deployment node in the application account.

7. The method of claim 2, wherein, After the multi-dimensional computing power information corresponding to the application account is collected at a fixed time, and the computing power efficiency of each deployment node is identified according to the multi-dimensional computing power information, the cloud computing power efficiency maintenance table of the application account is updated according to the multi-dimensional computing power information and the computing power efficiency of each deployment node, the method further includes: When the cloud computing power efficiency maintenance table of the application account is updated, the efficient deployment node is identified according to the current computing power efficiency of each deployment node in the cloud computing power efficiency maintenance table; The expansion demand of the efficient deployment node is detected according to the plurality of table items corresponding to each efficient deployment node in the cloud computing power efficiency maintenance table, and when it is detected that the target efficient deployment node has expansion demand, the to-be-expanded list is updated; When the first time node is reached, the to-be-expanded list is issued to the target computing power management personnel and the target computing power user, so that the target computing power user performs expansion operation for the target efficient deployment node according to the to-be-expanded list.

8. An electronic device, comprising: The electronic device includes: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the cloud computing power management method of any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to execute the cloud computing power management method of any one of claims 1-7 when executed.

10. A computer program product, characterised in that, The computer program product includes a computer program that, when executed by a processor, implements the cloud computing power management method according to any one of claims 1-7. The computer program product includes a computer program that, when executed by a processor, implements the cloud computing power management method according to any one of claims 1-7.

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