Adaptive computing resource configuration method, device, storage medium and electronic device

Abstract

The present application relates to an adaptive computing power resource configuration method, device, storage medium and electronic device, and relates to the field of computing power configuration technology, wherein the method includes: obtaining at least one target department in the target enterprise that has computing power demand; based on the target time period in which computing power is likely to be used by each target department and the corresponding at least one target computing power type, determining the key department with the highest degree of dependence on a single computing power type to be configured at the current time from each target department; determining the appropriate configuration size of the corresponding computing power type to be configured according to the target business type of at least one business being processed by each key department at the current time and the corresponding actual business progress; determining the computing power resource configuration plan corresponding to the target enterprise according to each of the appropriate configuration sizes, and configuring according to the computing power resource configuration plan. The present application has the effect of improving the adaptability of enterprises when performing computing power resource configuration.

Description

Technical Field

[0001] The present application relates to the technical field of computing power configuration, and specifically to an adaptive computing power resource configuration method, device, storage medium and electronic device. Background Art

[0002] Computing power is the ability of computer equipment or computing / data centers to process information, and is the ability of computer hardware and software to work together to perform certain computing needs. In a broader sense, computing power is the core driving force behind the development of cutting-edge technologies such as artificial intelligence, big data analysis, and cloud computing. Computing power resources refer to various resources used to provide computing power support, including computer equipment, servers, data centers, etc. These resources work together to complete complex computing tasks to meet the needs of different application scenarios. In addition, computing power resource allocation refers to the reasonable planning, allocation and management of computing power resources (such as CPU, GPU, storage, etc.) according to the business needs and goals of the enterprise to support the enterprise's data processing, analysis and decision-making activities.

[0003] At present, the way enterprises usually configure computing power resources is: relevant technical personnel in the enterprise evaluate the computing power needs of the enterprise, obtain the evaluation results, and finally manually formulate corresponding computing power resource configuration plans based on the evaluation results. However, the computing power needs of the enterprise may change dynamically with factors such as business characteristics and enterprise scale. Under this method, the matching between the computing power needs evaluated manually and the enterprise is poor, resulting in poor adaptability when configuring computing power resources for the enterprise. Summary of the invention

[0004] In order to improve the adaptability of an enterprise when configuring computing resources, the present application provides an adaptive computing resource configuration method, device, storage medium and electronic device.

[0005] In a first aspect of the present application, a method for adaptive computing resource configuration is provided, which specifically includes:

[0006] Obtain at least one target department in the target enterprise that has computing power requirements;

[0007] Based on the target time period during which computing power is likely to be used by each target department and the corresponding at least one target computing power type, determine from each target department the key department with the highest degree of dependence on the single computing power type to be configured at the current time, the target computing power type being the computing power type that is easily used within the target time period;

[0008] Determine the appropriate configuration size of the corresponding computing power type to be configured according to the target business type of at least one business being processed by each of the key departments at the current time and the corresponding actual business progress;

[0009] According to each of the appropriate configuration sizes, determine the computing power resource configuration plan corresponding to the target enterprise, and configure according to the computing power resource configuration plan.

[0010] By adopting the above technical solution, after obtaining the target departments with computing power demand, combined with the target time period in which each target department is prone to use computing power, and the computing power type that is easy to use in each target time period, determine the key departments with the highest degree of dependence on a single computing power type, that is, the computing power type to be configured, or the greatest possibility of use at the current time. Then, the target business type and the corresponding actual business progress of the business being processed by the key departments can be specifically referred to in the future, and the computing power demand of the target enterprise on the corresponding computing power type to be configured can be analyzed more targeted and reasonably, and the appropriate configuration size of the computing power type to be configured can be accurately determined. Finally, based on the determined appropriate configuration sizes, the computing power resource configuration scheme that is more suitable for the target enterprise is determined and computing power configuration is performed accordingly, thereby improving the adaptability of the enterprise when performing computing power resource configuration.

[0011] Optionally, based on the target time period in which computing power is likely to be used by each target department and the corresponding at least one target computing power type, determining from each target department a key department with the highest degree of dependence on a single computing power type to be configured at the current time, specifically includes:

[0012] Obtain the historical time periods of computing power usage of a single target department within the historical period, count the number of occurrences of each historical time period, and select the first historical time period from each of the historical time periods in descending order of the number of occurrences as the target time period, where the historical time period is the same as the current time period;

[0013] Counting the usage time of each historical computing power type used in the target period, and selecting the second historical computing power type from each historical computing power type in descending order of the usage time as the target computing power type for the corresponding target period;

[0014] Calculate a first weight for each target period and a second weight for each corresponding target computing power type, wherein the first weight is a ratio of the number of occurrences of each target period to the sum of the number of occurrences of all target periods, and the second weight is a ratio of the usage duration of a single target computing power type corresponding to the target period to the sum of the usage durations of all corresponding target computing power types;

[0015] According to the first weight of each target department and the corresponding second weights, a key department with the highest degree of dependence on the single computing power type to be configured at the current time is determined from among the target departments.

[0016] By adopting the above technical solution, the greater the number of occurrences, the more likely the target department is to use computing power in the corresponding historical period, thereby determining the target period where computing power is likely to be used; the longer the usage time, the more likely it is to occupy the corresponding historical computing power type during the target period; finally, combining the first weight of a single target department and the corresponding second weights, analyze and determine the corresponding target department's degree of dependence on the historical computing power type or possibility of use at the current time, and finally accurately screen out the key departments with the highest degree of dependence on a single computing power type to be configured.

[0017] Optionally, determining, from among the target departments, the key department with the highest degree of dependence on the single computing power type to be configured at the current time according to the first weight of each target department and the corresponding second weights, specifically includes:

[0018] Determine the target department including the current time in each corresponding target time period as an important department, determine the target time period including the current time as an important time period, and calculate the first product of the first weight of the important time period of each important department and the second weight of each corresponding target computing power type;

[0019] Summing the first products corresponding to the same target computing power type to obtain a corresponding sum of the first products, and if the sum of the first products is greater than a preset product sum threshold, determining the corresponding target computing power type as the computing power type to be configured;

[0020] A maximum first product is selected from the first products corresponding to the computing power type to be configured, and the important department corresponding to the maximum first product is determined as the key department with the highest degree of dependence on the corresponding computing power type to be configured.

[0021] By adopting the above technical solution, the larger the first product is, the easier it is for the important department to use the computing power resources of the corresponding target computing power type at the current time, and the larger the sum of the first products is, the more likely the target enterprise is to use or rely on the computing power resources of the corresponding target computing power type at the current time; if the sum of the first products is greater than the preset product sum threshold, it means that the possibility of using or relying on the computing power resources of the corresponding target computing power type at the current time is relatively large, then the corresponding target computing power type is determined as the computing power type to be configured at the current time, and finally, the maximum first product is selected from the first products corresponding to the computing power type to be configured, and the important department corresponding to the maximum first product is determined as the key department with the highest degree of dependence on the computing power resources of the computing power type to be configured at the current time, or the key department with the greatest possibility of use, so as to facilitate the subsequent reference to the key departments to determine the configuration size of the corresponding computing power type to be configured.

[0022] Optionally, the method further includes:

[0023] Calculate the second product of the first weight of each of the important departments and the corresponding second weights and sum them up to obtain the sum of the corresponding second products;

[0024] The computing power usage priority of the corresponding important department is determined according to the sum of the second products. The larger the second product is, the higher the computing power usage priority of the corresponding important department is.

[0025] By adopting the above technical solution, the larger the sum of the second products is, the greater the possibility that the corresponding important departments will use the computing power resources as a whole at the current time, and the higher the priority of using the computing power of the overall computing power resources configured by the enterprise, which helps to ensure that the enterprise's computing power resource management is more reasonable.

[0026] Optionally, determining the appropriate configuration size of the corresponding computing power type to be configured according to the target business type of at least one business being processed by each of the key departments at the current time and the corresponding actual business progress specifically includes:

[0027] Determine the historical processing business type in which the computing power usage time in a single key department exceeds the preset time, count the first occurrence frequency of each historical processing business type, and select the third historical processing business type from each historical processing business type in descending order of the first occurrence frequency to determine it as the business type that is easy to occupy computing power;

[0028] If there is a corresponding target business type being processed by the key department in each of the Yizhan computing power business types, the corresponding target business type is determined as the key business type;

[0029] Determine the historical high-occupancy computing power type of the historical business of the single key business type under the corresponding actual business progress, count the second occurrence frequency of each historical high-occupancy computing power type, and select the fourth number of historical high-occupancy computing power types from each of the historical high-occupancy computing power types in descending order of the second occurrence frequency to determine as the easy high-occupancy computing power type of the corresponding key business type, and the historical high-occupancy computing power type is the historical computing power type that occupies the most resources;

[0030] Calculate the third weight of each of the key business types and the corresponding fourth weight of each of the easy-to-occupy computing power types, the third weight being the ratio of the first occurrence frequency of each key business type to the sum of the first occurrence frequencies of all easy-to-occupy computing power business types, and the fourth weight being the ratio of the second occurrence frequency of a single easy-to-occupy computing power type corresponding to the key business type to the sum of the second occurrence frequencies of all corresponding easy-to-occupy computing power types;

[0031] According to the third weight and the corresponding fourth weights, an appropriate configuration size of the corresponding computing power type to be configured is determined.

[0032] By adopting the above technical solution, the greater the first occurrence frequency, the easier it is for the corresponding historical processing business type to occupy computing power, and then determine the business type that is easy to occupy computing power; the greater the second occurrence frequency, the easier it is for the key business type to occupy a large amount of the corresponding historical high computing power type under the corresponding actual task progress, and then determine the key business type that is easy to occupy high computing power. Finally, combined with the third weight of each key business type and the corresponding fourth weight of each high computing power type, analyze and determine the business that is easy to occupy computing power in the current time of the key department, and then accurately determine the appropriate configuration size of the computing power type to be configured.

[0033] Optionally, determining the appropriate configuration size of the corresponding computing power type to be configured according to the third weight and the corresponding fourth weights specifically includes:

[0034] If there is a corresponding computing power type to be configured in each of the easy-to-configure computing power types corresponding to a single key business type, the corresponding key business type is determined as the final business type, and the third product of the third weight of each final business type and the fourth weight of the corresponding computing power type to be configured is calculated and summed to obtain the sum of the third products;

[0035] Determine the high-occupancy computing power types other than the corresponding computing power types to be configured among the high-occupancy computing power types corresponding to the single key business type as reference computing power types, and calculate the fourth product of the third weight of each key business type and the fourth weight of the corresponding reference computing power types;

[0036] Summing the fourth products corresponding to the same reference computing power type to obtain a sum of the corresponding fourth products;

[0037] If the sum of each of the fourth products is smaller than the sum of the third products, then the appropriate configuration size of the corresponding computing power type to be configured is determined according to each of the final business types.

[0038] By adopting the above technical solution, the larger the sum of the third products, the more likely it is that this key department currently uses a large amount of computing power resources of the corresponding type of computing power to be configured. Furthermore, the larger the sum of the fourth products, combined with the target business type being processed, the more likely it is that the key department currently uses a large amount of computing power resources of the corresponding reference computing power type. Finally, if the sum of each fourth product is less than the sum of the third product, it means that the current key department is most likely to use a large amount of the corresponding type of computing power to be configured, and the current key department is most likely to rely on the corresponding type of computing power to be configured, thereby verifying that the type of computing power to be configured of the key department is confirmed correctly, so that the type of computing power resources that the target enterprise needs to configure is more reasonable and accurate.

[0039] Optionally, the method further includes:

[0040] Determining a maximum sum of fourth products from the sums of the fourth products, and if the maximum sum of the fourth products is greater than the sum of the third products, determining a reference computing power type corresponding to the maximum sum of the fourth products as a final configured computing power type;

[0041] If the sum of the maximum fourth products is equal to the sum of the third products, then counting a first number of key business types corresponding to the sum of the maximum fourth products and a second number of final business types corresponding to the sum of the third products;

[0042] When the first number is greater than the second number, determining the reference computing power type corresponding to the sum of the largest fourth products as the final configured computing power type;

[0043] The key department corresponding to the maximum sum of the fourth products is determined as the configuration reference department, and the computing power type to be configured corresponding to the configuration reference department is replaced with the final configuration computing power type, and the appropriate configuration size of the final configuration computing power type is determined based on at least one key business type corresponding to the maximum sum of the fourth products.

[0044] By adopting the above technical solution, if the sum of the largest fourth products is greater than the sum of the third products, it means that the computing power type that is most likely to be used by the key department at the current time is not the determined computing power type to be configured, then the reference computing power type corresponding to the largest sum of the fourth products is determined as the final configured computing power type; if the first number is greater than the second number, it means that the computing power resources of the corresponding reference computing power type can cover more businesses being processed, then the reference computing power type corresponding to the largest sum of the fourth products is determined as the final configured computing power type, and finally the computing power type to be configured determined by this configuration reference department is replaced with this final configured computing power type, so that the type of configured computing power resources is more reasonable.

[0045] In a second aspect of the present application, an adaptive computing resource configuration device is provided, specifically comprising:

[0046] An information acquisition module, used to acquire at least one target department in the target enterprise that has computing power demand;

[0047] A department screening module is used to determine, from each of the target departments, a key department with the highest degree of dependence on a single computing power type to be configured at the current time, based on a target time period in which computing power is likely to be used by each of the target departments and at least one corresponding target computing power type, wherein the target computing power type is a computing power type that is easily used within the target time period;

[0048] A configuration calculation module is used to determine the appropriate configuration size of the corresponding computing power type to be configured according to the target business type of at least one business being processed by each of the key departments at the current time and the corresponding actual business progress;

[0049] The computing power configuration module is used to determine the computing power resource configuration plan corresponding to the target enterprise according to each of the appropriate configuration sizes, and configure according to the computing power resource configuration plan.

[0050] By adopting the above technical solution, the information acquisition module obtains the target department in the target enterprise, and then the department screening module determines the key departments from each target department. Then the configuration calculation module determines the appropriate configuration size of the corresponding computing power type to be configured according to at least one target business type being processed by each key department at the current time and the actual business progress. Finally, the computing power configuration module determines the computing power resource configuration plan and configures it according to the computing power resource configuration plan.

[0051] In a third aspect of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is loaded and executed by a processor, the method steps described in any one of the first aspects are performed.

[0052] In a fourth aspect of the present application, an electronic device is provided, specifically comprising:

[0053] A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the processor is used to load and execute the computer program stored in the memory so that the electronic device performs the method as described in any one of the first aspects.

[0054] In summary, the present application includes at least one of the following beneficial technical effects: combining the target time period in which each target department is prone to use computing power, and the type of computing power that is easy to use in each target time period, determining the key departments that have the highest degree of dependence on a single computing power type, that is, the computing power type to be configured, or are most likely to use it at the current time, and then subsequently referring to the target business type and the corresponding actual business progress of the business being processed by the key departments, more targeted and reasonable analysis of the computing power requirements of the target enterprise on the corresponding computing power type to be configured, and then accurately determining the appropriate configuration size of the computing power type to be configured. Finally, based on the determined appropriate configuration sizes, determine the computing power resource configuration plan that is more suitable for the target enterprise at present and perform computing power configuration accordingly, thereby improving the adaptability of the enterprise when configuring computing power resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of an adaptive computing resource configuration method provided in an embodiment of the present application;

[0056] Figure 2 It is a flowchart of another adaptive computing power resource configuration method provided in an embodiment of the present application;

[0057] Figure 3 It is a structural diagram of an adaptive computing resource configuration device provided in an embodiment of the present application;

[0058] Figure 4 It is a structural diagram of another adaptive computing power resource configuration device provided in an embodiment of the present application.

[0059] Explanation of the accompanying drawings: 11. Information acquisition module; 12. Department screening module; 13. Configuration calculation module; 14. Computing power configuration module; 15. Usage setting module; 16. Type adjustment module. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0061] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0062] In the description of the embodiments of the present application, the term "and / or" is only a kind of association relationship describing the associated objects, indicating that there may be three kinds of relationships, for example, A and / or B, which can represent: A exists alone, B exists alone, and A and B exist at the same time. In addition, unless otherwise specified, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" can explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0063] See also Figure 1The present application embodiment discloses a flowchart of an adaptive computing resource configuration method, which can be implemented by a computer program or run on an adaptive computing resource configuration device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application, specifically including:

[0064] S101: Acquire at least one target department in the target enterprise that has computing power demand.

[0065] Specifically, in an embodiment of the present application, the target enterprise is an enterprise with a demand for computing power resource configuration, which may be an Internet enterprise. In other embodiments, it may also be a financial technology enterprise or an automobile enterprise. All departments in the target enterprise may be target departments with computing power needs. In other embodiments, some departments in the target enterprise are target departments with computing power needs. In addition, the execution subject of an adaptive computing power resource configuration method disclosed in an embodiment of the present application may be a server, and the server and the terminal are connected via a wireless network. The terminal may be a smart phone or a personal computer, and a client or applet related to automatic computing power configuration is installed in the terminal. The server is a background server corresponding to the client or applet. The background server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers.

[0066] Furthermore, a feasible way to obtain at least one target department is: the operation and maintenance personnel of the target enterprise opens the client or applet in the terminal, selects the "Enable adaptive configuration" option in the interface of the client or applet, and the client or applet sends an enable instruction to the server through the software development kit (Software Development Kit, SDK). The server obtains the enable instruction, sends a collection pop-up window of the target department to the terminal, and finally receives information such as the name of at least one target department sent by the terminal.

[0067] S102: Based on the target time period in which computing power is likely to be used by each target department and the corresponding at least one target computing power type, determine the key department that has the highest degree of dependence on a single computing power type to be configured at the current time from among the target departments, and the target computing power type is a computing power type that is easily used within the target time period.

[0068] Specifically, after the target department is determined, based on the historical monitoring records of the computing power usage of each target department using the preset Prometheus tool or Grafana tool, determine the historical time period in which the computing power usage of a single target department exists within the historical period, where the historical period is the same as the current period. For example, if the current period is the third quarter of 2024, then the historical period can be the third quarter of each year in the past two years; or, if the current period is October 2024, then the historical period can be October of each year in the past two years. Further, the number of occurrences of each historical period is counted. The greater the number of occurrences, the more likely the target department is to use computing power in the corresponding historical period. Then, in order of the number of occurrences from large to small, the historical period with the preset first number is selected from each historical period, and determined as the target period where computing power usage is likely to occur.

[0069] Further, based on the historical monitoring records, the usage time of the historical computing power types used in a single target period is determined, that is, the occupation or usage time of the computing power resources, and then the preset second number of historical computing power types is selected from each historical computing power type in order of usage time from large to small, and determined as the target computing power type for the corresponding target period, that is, the computing power type that is easy to use in the corresponding target period. Among them, in the embodiment of the present application, the computing power type includes but is not limited to CPU computing power, GPU computing power, FPGA computing power, and cloud computing computing power, etc., which are respectively applicable to different business scenarios and provide computing power support for different business scenarios. It should be noted that the historical monitoring records include but are not limited to information such as the duration of use of various computing power resources by different target departments, the type of computing power used, and the type of business using computing power.

[0070] Furthermore, the first weight of each target period and the second weight of each corresponding target computing power type are calculated. The first weight is the ratio of the number of occurrences of each target period to the sum of the number of occurrences of all target periods, and the second weight is the ratio of the usage time of a single target computing power type corresponding to the target period to the sum of the usage time of all corresponding target computing power types. Finally, according to the first weight of each target department and the corresponding second weights, the key department with the highest degree of dependence on a single computing power type to be configured at the current time is determined from all target departments, that is, the key department with a high probability of using a certain computing power type heavily at the current time. A feasible determination method is: determine the target department containing the current time in the corresponding target period as an important department, and determine the target period containing the current time as an important period. Then calculate the first product of the first weight of the important period of each important department and the second weight of each corresponding target computing power type. The larger the first product, the easier it is for the important department to use the computing power resources of the corresponding target computing power type at the current time. Furthermore, the sum of the first products corresponding to the same target computing power type is summed to obtain the corresponding sum of the first products. The larger the sum of the first products, the greater the possibility that the target enterprise currently uses or relies on the computing power resources of the corresponding target computing power type. If the sum of the first products is greater than the preset product sum threshold, it means that the possibility of using or relying on the computing power resources of the corresponding target computing power type at the current time is relatively large, then the corresponding target computing power type is determined as the computing power type to be configured at the current time, that is, the computing power type that needs to be configured for computing power resources. Finally, the maximum first product is selected from the first products corresponding to the computing power type to be configured, and the important department corresponding to the maximum first product is determined as the key department with the highest degree of dependence on the computing power resources of the computing power type to be configured at the current time, or the greatest possibility of use.

[0071] In other embodiments, the use priority of the computing power resources of the computing power type to be configured can be set for each important department corresponding to the sum of the first products. The larger the first product, the higher the priority of the corresponding important department in using the computing power resources of this computing power type to be configured. In another embodiment, after each important department is determined, the second product of the first weight of each important department and the corresponding second weights is calculated and summed to obtain the corresponding sum of the second products. The larger the sum of the second products, the greater the possibility of the corresponding important department to use the computing power resources as a whole at the current time, and then the computing power use priority of the overall computing power resources configured by the enterprise is higher, which helps to ensure that the enterprise computing power resource management is more reasonable.

[0072] S103: Determine the appropriate configuration size of the corresponding computing power type to be configured according to the target business type of at least one business being processed by each key department at the current time and the corresponding actual business progress.

[0073] Specifically, after the key departments are determined, it is necessary to determine the target business type of the business being processed by each key department and the corresponding actual business progress. A feasible determination method is: receiving the work progress reports of each target department sent by the terminal, and based on the work progress reports of the key departments, determine the target business type of the business being processed by a single key department and the corresponding actual business progress. Finally, call the preset configuration matching table, match the configuration values ​​of the computing power type to be configured for each corresponding target business type under the corresponding actual task progress from the configuration matching table, and finally sum up the various configuration values ​​to obtain the appropriate configuration size of the computing power type to be configured. Among them, the configuration matching table includes the computing power configuration size of the computing power type to be configured for the same business type under different task progress, which is specifically set by the operation and maintenance personnel based on experience.

[0074] S104: Determine the computing power resource configuration plan corresponding to the target enterprise based on each appropriate configuration size, and configure according to the computing power resource configuration plan.

[0075] Specifically, after referring to the target business type and the corresponding actual business progress of the business being processed by each key department and determining the appropriate configuration size of the corresponding computing power type to be configured, each computing power type to be configured and the corresponding appropriate configuration size are summarized to obtain the computing power resource configuration plan of the target enterprise at the current time, and this computing power resource configuration plan is sent to the terminal of the operation and maintenance personnel in the target enterprise. If the confirmation information sent by the terminal is received, the computing power resources of the corresponding computing power type are automatically configured for the target enterprise based on this computing power resource configuration plan through the preset automated operation and maintenance tool, thereby realizing dynamic and better adaptable computing power configuration for the enterprise. Among them, the automated operation and maintenance tool can be a Terraform tool, and in other embodiments, it can also be an Ansible tool.

[0076] See also Figure 2 , the embodiment of the present application discloses a flowchart of another adaptive computing resource configuration method, which can be implemented by a computer program or run on an adaptive computing resource configuration device based on the von Neumann system. The computer program can be integrated into an application or run as an independent tool application, specifically including:

[0077] S201: Acquire at least one target department in the target enterprise that has computing power demand.

[0078] S202: Based on the target time period in which computing power is likely to be used by each target department and the corresponding at least one target computing power type, determine the key department that has the highest degree of dependence on a single computing power type to be configured at the current time from among the target departments, and the target computing power type is a computing power type that is easily used within the target time period.

[0079] For details, please refer to steps S101-S102, which will not be described in detail here.

[0080] S203: Determine the historical processing business types in which the computing power usage time in a single key department exceeds the preset time, count the first occurrence frequency of each historical processing business type, and select the third historical processing business type from each historical processing business type in order of the first occurrence frequency from large to small, and determine it as the business type that is easy to occupy computing power.

[0081] S204: If there is a corresponding target business type being processed by the key department in each Yizhan computing power business type, the corresponding target business type is determined as the key business type.

[0082] S205: Determine the historical high computing power type of the historical business of a single key business type under the corresponding actual business progress, count the second occurrence frequency of each historical high computing power type, and select the fourth number of historical high computing power types from each historical high computing power type in order of the second occurrence frequency from large to small to determine it as the easy high computing power type of the corresponding key business type.

[0083] Specifically, based on the above-mentioned historical monitoring records, determine the historical processing business types in which the computing power usage time in a single key department exceeds the preset time, that is, the business types that have been processed historically, and count the first occurrence frequency of each historical processing business type. The larger the first occurrence frequency, the easier it is for the corresponding historical processing business type to occupy computing power. In order of the first occurrence frequency from large to small, select the third number of historical processing business types from each historical processing business type, and determine it as a business type that is easy to occupy computing power, that is, a business type that is easy to occupy computing power resources. If the target business type of the business that is being processed by this single key department exists in each business type that is easy to occupy computing power, the existing target business type will be determined as a key business type.

[0084] Furthermore, determine the historical high-occupancy computing power type of each key business type under the corresponding actual task progress, that is, the historical computing power type that occupies the most resources. Count the second occurrence frequency of each historical high-occupancy computing power type. The greater the second occurrence frequency, the easier it is for the key business type to occupy a large amount of the corresponding historical high-occupancy computing power type under the corresponding actual task progress. In order of the second occurrence frequency from large to small, select the fourth number of historical high-occupancy computing power types from each historical high-occupancy computing power type to determine it as the easy high-occupancy computing power type of the corresponding key business type, that is, the computing power type that is easy to occupy a large amount.

[0085] S206: Calculate the third weight of each key business type and the corresponding fourth weight of each high-capacity computing power type.

[0086] S207: Determine the appropriate configuration size of the corresponding computing power type to be configured according to the third weight and the corresponding fourth weights.

[0087] Specifically, the third weight of each key business type and the fourth weight of the corresponding types that are prone to high computing power are calculated. The third weight is the ratio of the first occurrence frequency of each key business type to the sum of the first occurrence frequencies of all business types that are prone to high computing power. The fourth weight is the ratio of the second occurrence frequency of a single type that is prone to high computing power corresponding to the key business type to the sum of the second occurrence frequencies of all corresponding types that are prone to high computing power.

[0088] Furthermore, if there is a corresponding key department's to-be-configured computing power type in each easy-to-occupy computing power type corresponding to a single key business type, then the corresponding key business type is determined as the final business type. The third product of the third weight of each final business type and the fourth weight of the corresponding computing power type to be configured is calculated and summed to obtain the sum of the third products. The larger the sum of the third products, the greater the possibility that this key department will use a large amount of computing power resources of the corresponding computing power type to be configured at the current time. Further, the easy-to-occupy computing power types other than the to-be-configured computing power types of the corresponding key departments in each easy-to-occupy computing power type corresponding to a single key business type are determined as reference computing power types. Next, the fourth product of the third weight of each key business type and the fourth weight of each corresponding reference computing power type is calculated, and then the fourth products corresponding to the same reference computing power type are summed to obtain the corresponding sum of the fourth products. The larger the sum of the fourth products, the greater the possibility that the key department will use a large amount of computing power resources of the corresponding reference computing power type at the current time in combination with the target business type of the business being processed. Finally, if the sum of each fourth product is less than the sum of the third product, it means that the current key department is most likely to use the corresponding type of computing power to be configured in large quantities, and the key department is most likely to rely on the corresponding type of computing power to be configured at the current time, thereby verifying that the type of computing power to be configured of the key department is correct. Further, according to the computing power demand of each final business type, the appropriate configuration size of the corresponding type of computing power to be configured is determined more accurately. A feasible determination method is: match the configuration size of the type of computing power to be configured for each final business type under the corresponding actual task progress in the preset matching table, and sum each configuration size to obtain the appropriate configuration size of the corresponding type of computing power to be configured. After the computing power resources are configured in this way, the needs of various types of computing power at the current time of the target enterprise can be better met. Among them, the matching table includes the computing power configuration size of the type of computing power to be configured for the same business type under different task progress, which is specifically set by the operation and maintenance personnel based on experience.

[0089] In other embodiments, the maximum sum of the fourth products is selected from the sums of the fourth products. If the maximum sum of the fourth products is greater than the sum of the third products, it means that the computing power type that is most likely to be used by the key department at the current time is not the determined computing power type to be configured, then the reference computing power type corresponding to the maximum sum of the fourth products is determined as the final configured computing power type; if the maximum sum of the fourth products is equal to the sum of the third products, then the first number of key business types corresponding to the maximum sum of the fourth products is counted, and the second number of final business types corresponding to the sum of the third products is counted. If the first number is greater than the second number, it means that the computing power resources of the corresponding reference computing power type can cover more businesses being processed, then the reference computing power type corresponding to the maximum sum of the fourth products is determined as the final configured computing power type. Finally, the key department corresponding to the maximum sum of the fourth products is determined as the configuration reference department, and the computing power type to be configured determined by the configuration reference department is replaced with the final configured computing power type, so that the type of configured computing power resources is more reasonable. Furthermore, referring to at least one key business type corresponding to the sum of the maximum fourth products and the corresponding actual task progress, the appropriate configuration size of the computing power resources of the final configured computing power type is determined.

[0090] In other embodiments, the priority of use of computing power resources of the final configured computing power type in the processing of the corresponding key business type can be determined based on the fourth product of each key business type corresponding to the sum of the maximum fourth products. The larger the fourth product, the greater the possibility that the corresponding key business type will use the final configured computing power type, and the greater the corresponding usage priority, so that the computing power resource allocation is more reasonable and the development of various businesses in the target enterprise is better guaranteed.

[0091] S208: Determine the computing power resource configuration plan corresponding to the target enterprise based on each appropriate configuration size, and configure according to the computing power resource configuration plan.

[0092] For details, please refer to step S104, which will not be described in detail here.

[0093] The implementation principle of an adaptive computing power resource configuration method in the embodiment of the present application is as follows: Combined with the target time period in which computing power is likely to be used by each target department, and the computing power type that is easy to use in each target time period, determine the key departments with the highest degree of dependence on a single computing power type, that is, the computing power type to be configured, or the greatest possibility of use at the current time, and then refer to the target business type and the corresponding actual business progress of the business being processed by the key department in detail, and analyze the computing power requirements of the target enterprise on the corresponding computing power type to be configured in a more targeted and reasonable manner, and then accurately determine the appropriate configuration size of the computing power type to be configured. Finally, based on the determined appropriate configuration sizes, determine the computing power resource configuration plan that is more suitable for the target enterprise at present, and perform computing power configuration accordingly, thereby improving the adaptability of the enterprise when performing computing power resource configuration.

[0094] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0095] See also Figure 3 , which is a schematic diagram of the structure of the adaptive computing resource configuration device provided in the embodiment of the present application. The adaptive computing resource configuration device can be implemented as all or part of the device through software, hardware or a combination of both. The device includes an information acquisition module 11, a department screening module 12, a configuration calculation module 13 and a computing power configuration module 14.

[0096] An information acquisition module 11 is used to acquire at least one target department in the target enterprise that has computing power demand;

[0097] The department screening module 12 is used to determine the key department with the highest degree of dependence on the single computing power type to be configured at the current time from each target department based on the target time period in which computing power is likely to be used by each target department and the corresponding at least one target computing power type, and the target computing power type is the computing power type that is easily used in the target time period;

[0098] Configuration calculation module 13, used to determine the appropriate configuration size of the corresponding computing power type to be configured according to the target business type of at least one business being processed by each key department at the current time and the corresponding actual business progress;

[0099] The computing power configuration module 14 is used to determine the computing power resource configuration plan corresponding to the target enterprise according to each appropriate configuration size, and configure according to the computing power resource configuration plan.

[0100] Optionally, the department screening module 12 is specifically used for:

[0101] Obtain the historical time periods of computing power usage of a single target department within the historical period, count the number of occurrences of each historical period, and select the first historical period from each historical period in descending order of the number of occurrences as the target period. The historical period is the same as the current period.

[0102] Count the usage time of the historical computing power types used in each target period, and select the second historical computing power type from each historical computing power type in descending order of usage time to determine it as the target computing power type for the corresponding target period;

[0103] Calculate the first weight of each target period and the second weight of each corresponding target computing power type, where the first weight is the ratio of the number of occurrences of each target period to the sum of the number of occurrences of all target periods, and the second weight is the ratio of the usage time of a single target computing power type corresponding to the target period to the sum of the usage time of all corresponding target computing power types;

[0104] According to the first weight of each target department and the corresponding second weights, the key department with the highest degree of dependence on the single computing power type to be configured at the current time is determined from among the target departments.

[0105] Optionally, the department screening module 12 is specifically used for:

[0106] Determine the target department that contains the current time in each corresponding target time period as an important department, determine the target time period that contains the current time as an important time period, and calculate the first product of the first weight of the important time period of each important department and the second weight of each corresponding target computing power type;

[0107] The first products corresponding to the same target computing power type are summed to obtain the corresponding sum of the first products. If the sum of the first products is greater than a preset product sum threshold, the corresponding target computing power type is determined as the computing power type to be configured;

[0108] The largest first product is selected from the first products corresponding to the computing power type to be configured, and the important department corresponding to the largest first product is determined as the key department with the highest degree of dependence on the corresponding computing power type to be configured.

[0109] Optional, such as Figure 4 As shown, the device also includes a usage setting module 15, which is specifically used to:

[0110] Calculate the second product of the first weight of each important department and the corresponding second weights and sum them up to obtain the sum of the corresponding second products;

[0111] According to the sum of the second products, the computing power utilization priority of the corresponding important departments is determined. The larger the second product is, the higher the computing power utilization priority of the corresponding important departments is.

[0112] Optionally, a calculation module 13 is configured to:

[0113] Determine the historical processing business types in which the computing power usage time in a single key department exceeds the preset time, count the first occurrence frequency of each historical processing business type, and select the third historical processing business type from each historical processing business type in descending order of the first occurrence frequency to determine it as the business type that is easy to occupy computing power;

[0114] If there is a corresponding target business type being processed by the key department in each Yizhan computing power business type, the corresponding target business type will be determined as the key business type;

[0115] Determine the historical high-occupancy computing power type of the historical business of a single key business type under the corresponding actual business progress, count the second occurrence frequency of each historical high-occupancy computing power type, and select the fourth historical high-occupancy computing power type from each historical high-occupancy computing power type in descending order of the second occurrence frequency to determine it as the easy high-occupancy computing power type of the corresponding key business type. The historical high-occupancy computing power type is the historical computing power type that occupies the most resources;

[0116] Calculate the third weight of each key business type and the fourth weight of each corresponding easy-to-occupy computing power type, the third weight is the ratio of the first occurrence frequency of each key business type to the sum of the first occurrence frequencies of all easy-to-occupy computing power business types, and the fourth weight is the ratio of the second occurrence frequency of a single easy-to-occupy computing power type corresponding to the key business type to the sum of the second occurrence frequencies of all corresponding easy-to-occupy computing power types;

[0117] According to the third weight and the corresponding fourth weights, the appropriate configuration size of the corresponding computing power type to be configured is determined.

[0118] Optionally, a calculation module 13 is configured to:

[0119] If there is a corresponding computing power type to be configured in each easy-to-occupy computing power type corresponding to a single key business type, the corresponding key business type is determined as the final business type, and the third product of the third weight of each final business type and the fourth weight of the corresponding computing power type to be configured is calculated and summed to obtain the sum of the third products;

[0120] Determine the high-occupancy computing power types among the high-occupancy computing power types corresponding to the single key business type, except for the corresponding computing power type to be configured, as the reference computing power type, and calculate the fourth product of the third weight of each key business type and the fourth weight of each corresponding reference computing power type;

[0121] Sum the fourth products corresponding to the same reference computing power type to obtain the sum of the corresponding fourth products;

[0122] If the sum of the fourth products is less than the sum of the third products, the appropriate configuration size of the corresponding computing power type to be configured is determined according to each final business type.

[0123] Optionally, the device further includes a type adjustment module 16, which is specifically used to:

[0124] Determine the maximum sum of the fourth products from the sums of the fourth products. If the maximum sum of the fourth products is greater than the sum of the third products, determine the reference computing power type corresponding to the maximum sum of the fourth products as the final configured computing power type.

[0125] If the sum of the largest fourth products is equal to the sum of the third products, then counting the first number of key business types corresponding to the sum of the largest fourth products and the second number of final business types corresponding to the sum of the third products;

[0126] When the first number is greater than the second number, the reference computing power type corresponding to the sum of the largest fourth products is determined as the final configured computing power type;

[0127] The key department corresponding to the maximum sum of the fourth products is determined as the configuration reference department, and the computing power type to be configured corresponding to the configuration reference department is replaced with the final configuration computing power type, and the appropriate configuration size of the final configuration computing power type is determined based on at least one key business type corresponding to the maximum sum of the fourth products.

[0128] It should be noted that the adaptive computing resource configuration device provided in the above embodiment only uses the division of the above functional modules as an example when executing the adaptive computing resource configuration method. In actual applications, the above functional allocation can be completed by different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the adaptive computing resource configuration device provided in the above embodiment and the adaptive computing resource configuration method embodiment belong to the same concept. The implementation process is detailed in the method embodiment and will not be repeated here.

[0129] An embodiment of the present application further discloses a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, an adaptive computing power resource configuration method of the above embodiment is adopted.

[0130] Among them, the computer program can be stored in a computer-readable medium, the computer program includes computer program code, the computer program code can be in the form of source code, object code, executable file or certain middleware, etc. The computer-readable medium includes any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above-mentioned components.

[0131] Among them, through this computer-readable storage medium, an adaptive computing resource configuration method of the above embodiment is stored in a computer-readable storage medium, and is loaded and executed on a processor to facilitate the storage and application of the above method.

[0132] An embodiment of the present application also discloses an electronic device, in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, the above-mentioned adaptive computing power resource configuration method is adopted.

[0133] The electronic device may be a desktop computer, a laptop computer, a cloud server or other electronic device, and the electronic device includes but is not limited to a processor and a memory. For example, the electronic device may also include input and output devices, a network access device, and a bus.

[0134] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0135] Among them, the memory can be an internal storage unit of the electronic device, such as a hard disk or memory of the electronic device, or it can be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital card (SD) or a flash memory card (FC) equipped on the electronic device. Moreover, the memory can also be a combination of an internal storage unit and an external storage device of the electronic device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or is to be output, and this application does not impose any restrictions on this.

[0136] Among them, through this electronic device, an adaptive computing resource configuration method of the above embodiment is stored in the memory of the electronic device, and is loaded and executed on the processor of the electronic device for easy use.

[0137] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. An adaptive computing resource configuration method, characterized in that: The method comprises: Obtain at least one target department in the target enterprise that has computing power requirements; Based on the target time period during which computing power is likely to be used by each target department and the corresponding at least one target computing power type, determine from each target department the key department with the highest degree of dependence on the single computing power type to be configured at the current time, the target computing power type being the computing power type that is easily used within the target time period; According to the target business type of at least one business being processed by each of the key departments at the current time and the corresponding actual business progress, determining the appropriate configuration size of the corresponding computing power type to be configured includes: Determine the historical processing business type in which the computing power usage time in a single key department exceeds the preset time, count the first occurrence frequency of each historical processing business type, and select the third historical processing business type from each historical processing business type in descending order of the first occurrence frequency to determine it as the business type that is easy to occupy computing power; If there is a corresponding target business type being processed by the key department in each of the Yizhan computing power business types, the corresponding target business type is determined as the key business type; Determine the historical high computing power type of the historical business of the single key business type under the corresponding actual business progress, count the second occurrence frequency of each historical high computing power type, and select the fourth number of historical high computing power types from each of the historical high computing power types in descending order of the second occurrence frequency to determine as the easy high computing power type of the corresponding key business type; Calculate the third weight of each of the key business types and the corresponding fourth weight of each of the easy-to-occupy computing power types, the third weight being the ratio of the first occurrence frequency of each key business type to the sum of the first occurrence frequencies of all easy-to-occupy computing power business types, and the fourth weight being the ratio of the second occurrence frequency of a single easy-to-occupy computing power type corresponding to the key business type to the sum of the second occurrence frequencies of all corresponding easy-to-occupy computing power types; Determining the appropriate configuration size of the corresponding computing power type to be configured according to the third weight and the corresponding fourth weights includes: If there is a corresponding computing power type to be configured in each of the easy-to-configure computing power types corresponding to a single key business type, the corresponding key business type is determined as the final business type, and the third product of the third weight of each final business type and the fourth weight of the corresponding computing power type to be configured is calculated and summed to obtain the sum of the third products; Determine the high-occupancy computing power types other than the corresponding computing power types to be configured among the high-occupancy computing power types corresponding to the single key business type as reference computing power types, and calculate the fourth product of the third weight of each key business type and the fourth weight of the corresponding reference computing power types; Summing the fourth products corresponding to the same reference computing power type to obtain a sum of the corresponding fourth products; If the sum of the fourth products is less than the sum of the third products, determining the appropriate configuration size of the corresponding computing power type to be configured according to each final business type; According to each of the appropriate configuration sizes, determine the computing power resource configuration plan corresponding to the target enterprise, and configure according to the computing power resource configuration plan.

2. The method for adaptive computing resource configuration according to claim 1, characterized in that: The step of determining, based on the target time period during which computing power is likely to be used by each target department and the corresponding at least one target computing power type, from each target department, the key department with the highest degree of dependence on the single computing power type to be configured at the current time specifically includes: Obtain the historical time periods of computing power usage of a single target department within the historical period, count the number of occurrences of each historical time period, and select the first historical time period from each of the historical time periods in descending order of the number of occurrences as the target time period, where the historical time period is the same as the current time period; Counting the usage time of each historical computing power type used in the target period, and selecting the second historical computing power type from each historical computing power type in descending order of the usage time as the target computing power type for the corresponding target period; Calculate a first weight for each target period and a second weight for each corresponding target computing power type, wherein the first weight is a ratio of the number of occurrences of each target period to the sum of the number of occurrences of all target periods, and the second weight is a ratio of the usage duration of a single target computing power type corresponding to the target period to the sum of the usage durations of all corresponding target computing power types; According to the first weight of each target department and the corresponding second weights, a key department with the highest degree of dependence on the single computing power type to be configured at the current time is determined from among the target departments.

3. The method for adaptive computing resource configuration according to claim 2, characterized in that: Determining, from the target departments, the key departments with the highest degree of dependence on the single computing power type to be configured at the current time according to the first weights of the target departments and the corresponding second weights specifically includes: Determine the target department including the current time in each corresponding target time period as an important department, determine the target time period including the current time as an important time period, and calculate the first product of the first weight of the important time period of each important department and the second weight of each corresponding target computing power type; Summing the first products corresponding to the same target computing power type to obtain a corresponding sum of the first products, and if the sum of the first products is greater than a preset product sum threshold, determining the corresponding target computing power type as the computing power type to be configured; A maximum first product is selected from the first products corresponding to the computing power type to be configured, and the important department corresponding to the maximum first product is determined as the key department with the highest degree of dependence on the corresponding computing power type to be configured.

4. The method for adaptive computing resource configuration according to claim 3, characterized in that: The method further comprises: Calculate the second product of the first weight of each of the important departments and the corresponding second weights and sum them up to obtain the sum of the corresponding second products; The computing power usage priority of the corresponding important department is determined according to the sum of the second products. The larger the second product is, the higher the computing power usage priority of the corresponding important department is.

5. The method for adaptive computing resource configuration according to claim 1, characterized in that: The method further comprises: Determining a maximum sum of fourth products from the sums of the fourth products, and if the maximum sum of the fourth products is greater than the sum of the third products, determining a reference computing power type corresponding to the maximum sum of the fourth products as a final configured computing power type; If the sum of the maximum fourth products is equal to the sum of the third products, then counting a first number of key business types corresponding to the sum of the maximum fourth products and a second number of final business types corresponding to the sum of the third products; When the first number is greater than the second number, determining the reference computing power type corresponding to the sum of the largest fourth products as the final configured computing power type; The key department corresponding to the maximum sum of the fourth products is determined as the configuration reference department, and the computing power type to be configured corresponding to the configuration reference department is replaced with the final configuration computing power type, and the appropriate configuration size of the final configuration computing power type is determined based on at least one key business type corresponding to the maximum sum of the fourth products.

6. An adaptive computing resource configuration device, used to implement the adaptive computing resource configuration method according to any one of claims 1 to 5, characterized in that: include: An information acquisition module (11) is used to acquire at least one target department in the target enterprise that has computing power demand; A department screening module (12) is used to determine, from each of the target departments, a key department with the highest degree of dependence on a single computing power type to be configured at the current time, based on a target time period in which computing power is likely to be used by each of the target departments and at least one corresponding target computing power type, wherein the target computing power type is a computing power type that is likely to be used within the target time period; A configuration calculation module (13) is used to determine the appropriate configuration size of the corresponding computing power type to be configured according to the target business type of at least one business being processed by each of the key departments at the current time and the corresponding actual business progress; A computing power configuration module (14) is used to determine the computing power resource configuration plan corresponding to the target enterprise according to each of the suitable configuration sizes, and configure according to the computing power resource configuration plan.

7. A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, characterized in that: When the computer program is loaded and executed by a processor, the method according to any one of claims 1 to 5 is adopted.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor loads and executes the computer program, the method according to any one of claims 1 to 5 is adopted.

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