Digital collaborative cloud computing resource optimization management system based on AI

Abstract

The invention relates to the technical field of resource management, and particularly discloses an AI-based digital collaborative cloud computing resource optimization management system, which comprises a data recording module used for recording the data processing amount of different applications in a computer in the historical use process, by combining two groups of data, namely data processing amount of different applications in a computer in a historical use process and running state data of the different applications in the current use process, the processor resource requirements of the different applications at the time point can be evaluated; the allocation result is dynamically allocated based on the current running states of the different applications, so that the rationality of resource allocation can be improved, and the situation that data processing of the applications in the using process is suddenly increased and exceeds an upper limit value is avoided; therefore, the utilization efficiency of processor resources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource management, and in particular to an AI-based digital collaborative cloud computing resource optimization management system. Background Art

[0002] Resource optimization refers to the process of improving resource utilization efficiency and benefits by rationally allocating and effectively utilizing resources under the premise of meeting certain conditions. In actual scenarios, the most common method is to optimize the computer's processor resources to improve processor utilization efficiency, reduce resource waste, and thus improve the overall performance of the system.

[0003] Traditional computer resource optimization management systems generally free up processor resources and improve system performance by shutting down unnecessary background programs, or adjust resource usage limits based on user priority data for different applications and application development data to prevent a single application from taking up too much processor resources, thereby achieving processor resource optimization management.

[0004] Traditional computer resource optimization management systems in the prior art generally adjust the upper limit of resource usage based on the priority data set by users for different applications and the development data of the applications. However, since the amount of data processed by an application increases significantly during use, once the upper limit is exceeded, it will affect the normal operation of the application, resulting in uneven allocation of processor resources and reduced utilization efficiency of processor resources. Summary of the invention

[0005] The purpose of this invention is to provide an AI-based digital collaborative cloud computing resource optimization management system to solve the following technical problems: How to dynamically adjust the processor resource allocation for different applications to improve the utilization efficiency of processor resources.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An AI-based digital collaborative cloud computing resource optimization management system, the system comprising: A data recording module is used to record the data processing volume of different applications in the computer during historical use; A data collection module is used to collect the running status data of different applications when the computer is currently in use; The cloud computing module is used to combine the data collected in the data collection module with the data in the data recording module to calculate the processor resource demand index of different applications at different time points during the current use process; The AI ​​evaluation module is used to evaluate the processor resource requirements of different applications at that time point based on the calculation results in the data analysis module; The data decision module is used to make decisions on resource allocation for different applications based on the evaluation results of the data evaluation module; The resource allocation module is used to allocate processor resources to different applications based on the decision results of the data decision module and the calculation results in the data analysis module.

[0007] Furthermore, the data collected in the data collection module includes: The memory usage, computing intensity, usage time and number of clicks of different applications on the computer.

[0008] Furthermore, the computing process in the cloud computing module includes: By formula Calculate the data processing volume dispersion coefficient of the ath application in the past period of time ; Where a is any application in the computer, i is any use of the application, n is the number of times the application has been used in the past period of time, and , is the total data processing volume used by the a-th application for the i-th time in the past period of time, is the total number of minutes when the a-th application was used for the i-th time in the past period of time. For all The average value of For all The maximum value in For all The minimum value in is the proportionality coefficient, which is set based on empirical fitting.

[0009] Furthermore, the computing process in the cloud computing module also includes: By calculating the discrete coefficient of the data processing volume of the a-th application in the past period of time Assign values ​​to generate a weighted impact value of the ath application that is between 1 and 5 and decreases as the discrete coefficient increases; Among them, the data processing volume dispersion coefficient of the ath application in the past period of time is The corresponding weight impact value of the a-th application is defined as .

[0010] Furthermore, the computing process in the cloud computing module also includes: By formula Calculate the processor resource demand index of the ath application at the yth time point during the current use process ; Among them, y is the data collected at a fixed time interval during the current use process, is the total number of data collection times at the yth time point during the current use process, is the data processing volume of the ath application at the yth time point during the current use process, for The standard value of is the memory usage of the ath application at the yth time point during the current use. for The standard value of is the computational intensity influence coefficient of the a-th application, which is set based on empirical fitting. is the usage time of the ath application at the yth time point during the current usage process, is the total usage time of the computer at the yth time point, is the number of times the ath application is clicked at the yth time point during the current use. for The standard value of .

[0011] Furthermore, the evaluation process of the AI ​​evaluation module includes: By calculating the processor resource demand index of the a-th application at the y-th time point in the current use process The preset processor resource demand index threshold range Make a comparison; like , the system determines that the application requires less processor resources; like , the system determines that the processor resources required by the application are medium; like , the system determines that the application requires more processor resources.

[0012] Furthermore, the decision-making process of the data decision module includes: When it is determined that the application requires less processor resources, it is defined as a low-demand application, and based on the amount of processor resources currently occupied by the application, the amount of processor resources allocated is reduced; When it is determined that the processor resources required by the application are medium, the processor resource allocation amount is not adjusted; When it is determined that the application requires a large amount of processor resources, it is defined as a high-demand application, and the amount of processor resources allocated is increased based on the amount of processor resources currently occupied by the application.

[0013] Furthermore, the adjustment process of the resource allocation module includes: When it is determined that the processor resources required by the a-th application are small; By formula Calculate the adjusted processor resource allocation for the ath low-demand application at the yth time point ; When it is determined that the a-th application requires more processor resources; By formula Calculate the adjusted processor resource allocation for the ath high-demand application at the yth time point ; in, The preset processor resource allocation for the ath low-demand application, The preset processor resource allocation for the a-th high-demand application, It is the adjustment coefficient comparison table function, because according to the empirical data The degree to which the range of values ​​affects the amount of processor resource allocation is obtained based on test data.

[0014] Beneficial effects of the present invention: (1) The present invention can evaluate the processor resource requirements of different applications at a given time point by combining two sets of data: the data processing volume of different applications in the computer during historical use and the running status data of different applications during current use. The processor resource allocation of different applications can then be dynamically adjusted based on the evaluation results. Since the allocation results are dynamically adjusted based on the current running status of different applications, the rationality of resource allocation can be improved, avoiding the situation where the normal operation of an application is affected by a sudden increase in data processing during use exceeding an upper limit, thereby improving the utilization efficiency of processor resources.

[0015] (2) The present invention calculates the processor resource demand index of the ath application at the yth time point during the current use process. The preset processor resource demand index threshold range By comparing the processor resources required by different applications, we can determine the amount of processor resources needed, thereby providing accurate data for subsequent decisions on how to adjust the resource allocation of the application, ensuring the accuracy of the decision results, thereby achieving processor resource optimization management and improving processor utilization.

[0016] (3) The present invention combines the processor resource demand index of the ath application at the yth time point in the current use process The preset processor resource demand index threshold range The comparison results can be used to make accurate decisions on the resource allocation methods for different applications, including reducing the processor resource allocation for low-demand applications and increasing the processor resource allocation for high-demand applications. By dynamically adjusting the processor resource allocation for different applications, it can ensure that the operating stability of high-demand applications can be increased while low-demand applications run normally, thereby achieving optimal allocation of processor resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 It is a schematic block diagram of an AI-based digital collaborative cloud computing resource optimization management system in the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, in one embodiment, the present application provides an AI-based digital collaborative cloud computing resource optimization management system, the system comprising: A data recording module is used to record the data processing volume of different applications in the computer during historical use; A data collection module is used to collect the running status data of different applications when the computer is currently in use; The cloud computing module is used to combine the data collected in the data collection module with the data in the data recording module to calculate the processor resource demand index of different applications at different time points during the current use process; The AI ​​evaluation module is used to evaluate the processor resource requirements of different applications at that time point based on the calculation results in the data analysis module; The data decision module is used to make decisions on resource allocation for different applications based on the evaluation results of the data evaluation module; A resource allocation module is used to allocate processor resources to different applications based on the decision results of the data decision module and the calculation results of the data analysis module; Through the above technical solution, this example provides a data recording module for recording the data processing volume of different applications in the computer during historical use. When the computer is running, the data collection module is first used to collect the running status data of different applications in the current use of the computer. After that, the cloud computing module can be used to combine the data collected in the data collection module with the data in the data recording module to calculate the processor resource demand index of different applications at different time points during the current use process, and the AI ​​evaluation module is combined with the calculation results in the data analysis module to evaluate the processor resource demand of different applications at that time point. After obtaining the evaluation results, the data decision module can be combined with the evaluation results of the data evaluation module to make decisions on resource allocation for different applications, and the resource allocation module can be combined with the decision results of the data decision module and the calculation results in the data analysis module to allocate processor resources for different applications. Through such a setting, this example can evaluate the processor resource requirements of different applications at that point in time by combining the data processing volume of different applications in the computer during historical use and the running status data of different applications during current use. Then, the processor resource allocation of different applications can be dynamically adjusted based on the evaluation results. Since the allocation result is dynamically allocated based on the current running status of different applications, the rationality of resource allocation can be improved, avoiding the situation where the normal operation of the application is affected due to a sudden increase in data processing during the use of the application exceeding the upper limit, thereby improving the utilization efficiency of processor resources.

[0021] The data collected in the data collection module includes: The memory usage, computing intensity, usage time and number of clicks of different applications in the computer; Through the above technical solution, this example provides data collected in the data collection module, including the memory usage, computing intensity, usage time and number of clicks of different applications in the computer. Applications with excessively high memory usage require more processor resources to ensure the stability of operation. High computing intensity applications also require more processor resources to process large amounts of data and complex calculations. Therefore, this data can directly reflect the processor resource requirements of different applications, and the usage time and number of clicks of different applications can reflect whether different applications are background applications. Background applications can reduce their processor resources and give priority to foreground applications, thereby realizing processor resource optimization management.

[0022] The computing process in the cloud computing module includes: By formula Calculate the data processing volume dispersion coefficient of the ath application in the past period of time ; Where a is any application in the computer, i is any use of the application, n is the number of times the application has been used in the past period of time, and , is the total data processing volume used by the a-th application for the i-th time in the past period of time, is the total number of minutes when the a-th application was used for the i-th time in the past period of time. For all The average value of For all The maximum value in For all The minimum value in is the proportionality coefficient, which is set based on empirical fitting; Through the above technical solution, this example provides the data processing volume dispersion coefficient of the ath application in the past period of time , can be obtained by formula The data can be obtained by calculation. This data can reflect the fluctuation of the data processing volume of the ath application in the past period of time. Since the background applications in the computer are generally in a "dormant" state, their data processing volume will increase only when they are updated or optimized, so the fluctuation of their data processing volume in the past period of time will be large. The data processing volume of the application currently being used is relatively stable during use, so the fluctuation of its data processing volume in the past period of time will be small. By setting it in this way, the discrete coefficient of the data processing volume of the ath application in the past period of time can be obtained. Determine whether the application is a background application or an application currently being used by the user, thereby providing data support for subsequent adjustment of processor resource allocation to ensure the rationality of resource allocation.

[0023] The computing process in the cloud computing module also includes: By calculating the discrete coefficient of the data processing volume of the a-th application in the past period of time Assign values ​​to generate a weighted impact value of the ath application that is between 1 and 5 and decreases as the discrete coefficient increases; Among them, the data processing volume dispersion coefficient of the ath application in the past period of time is The corresponding weight impact value of the a-th application is defined as ; Through the above technical solution, this example provides a discrete coefficient of the data processing volume of the ath application in the past period of time The process of assigning values; As an example, The value criteria are as follows: It should be noted that the dispersion coefficient of the data processing volume of the ath application in the past period of time is The larger the coefficient is, the smaller the weight impact value of the corresponding a-th application is. This is because the greater the fluctuation value of the data processing volume of the a-th application in the past period of time, the greater the fluctuation value is, indicating that the application is generally in a "dormant" state. Its data processing volume will only increase when it is updated or optimized, which can reduce the resource allocation of its processor. On the contrary, the dispersion coefficient of the data processing volume of the a-th application in the past period of time is The smaller it is, the greater the weight impact value of the corresponding a-th application is, because the fluctuation value of the data processing volume of the a-th application in the past period of time is smaller, which means that the data processing volume of the application when in use is relatively stable. For applications that users are currently using, it is necessary to increase the resource allocation of its processor to ensure the stability of the application.

[0024] The computing process in the cloud computing module also includes: By formula Calculate the processor resource demand index of the ath application at the yth time point during the current use process ; Among them, y is the data collected at a fixed time interval during the current use process, is the total number of data collection times at the yth time point during the current use process, is the data processing volume of the ath application at the yth time point during the current use process, for The standard value can be selected and set according to the allowable error in the empirical data. is the memory usage of the ath application at the yth time point during the current use. for The standard value can be selected and set according to the allowable error in the empirical data. is the computational intensity influence coefficient of the a-th application, which is set based on empirical fitting. is the usage time of the ath application at the yth time point during the current usage process, is the total usage time of the computer at the yth time point, is the number of times the ath application is clicked at the yth time point during the current use. for The standard value of the above standard value can be selected and set according to the allowable error in the empirical data; Through the above technical solution, this example provides the processor resource demand index of the ath application at the yth time point in the current use process: , can be obtained by formula Obviously, the greater the data processing volume and memory usage of the a-th application at the y-th time point during the current use, the shorter the usage time of the a-th application at the y-th time point during the current use, and the more times the a-th application is clicked from the beginning of use to the y-th time point during the current use, the processor resource demand index of the a-th application at the y-th time point during the current use is The larger the value, the more processor resources are needed to improve and ensure the running stability of the application. On the contrary, the smaller the data processing volume and memory usage of the a-th application at the y-th time point during the current use, the longer the usage time of the a-th application at the y-th time point during the current use, and the fewer the number of clicks of the a-th application from the beginning of use to the y-th time point during the current use, the processor resource demand index of the a-th application at the y-th time point during the current use is. The smaller the value, the more likely it is that the application is running in the background, and the amount of processor resources allocated can be appropriately reduced; Through this calculation method, the required amount of resources for different applications can be analyzed, thereby providing accurate data support for the subsequent resource allocation of different applications to ensure the rationality of resource allocation.

[0025] The evaluation process of the AI ​​evaluation module includes: By calculating the processor resource demand index of the a-th application at the y-th time point in the current use process The preset processor resource demand index threshold range Make a comparison; like , the system determines that the application requires less processor resources; like , the system determines that the processor resources required by the application are medium; like ,The system determines that the application requires more processor resources; Through the above technical solution, this example calculates the processor resource demand index of the ath application at the yth time point during the current use process The preset processor resource demand index threshold range By comparing the processor resources required by different applications, we can determine the amount of processor resources needed, thereby providing accurate data for subsequent decisions on how to adjust the resource allocation of the application, ensuring the accuracy of the decision results, thereby achieving processor resource optimization management and improving processor utilization.

[0026] The decision-making process of the data decision module includes: When it is determined that the application requires less processor resources, it is defined as a low-demand application, and based on the amount of processor resources currently occupied by the application, the amount of processor resources allocated is reduced; When it is determined that the processor resources required by the application are medium, the processor resource allocation amount is not adjusted; When it is determined that the application requires a lot of processor resources, it is defined as a high-demand application, and based on the amount of processor resources currently occupied by the application, the amount of processor resources allocated is increased; Through the above technical solution, this example combines the processor resource demand index of the ath application at the yth time point in the current use process The preset processor resource demand index threshold range The comparison results can be used to make accurate decisions on the resource allocation methods for different applications, including reducing the processor resource allocation for low-demand applications and increasing the processor resource allocation for high-demand applications. By dynamically adjusting the processor resource allocation for different applications, it can ensure that the operating stability of high-demand applications can be increased while low-demand applications run normally, thereby achieving optimal allocation of processor resources.

[0027] The adjustment process of the resource allocation module includes: When it is determined that the processor resources required by the a-th application are small; By formula Calculate the adjusted processor resource allocation for the ath low-demand application at the yth time point ; When it is determined that the a-th application requires more processor resources; By formula Calculate the adjusted processor resource allocation for the ath high-demand application at the yth time point ; in, The preset processor resource allocation for the ath low-demand application, The preset processor resource allocation for the a-th high-demand application, It is the adjustment coefficient comparison table function, because according to the empirical data The extent to which the range of values ​​affects the amount of processor resource allocation is obtained based on test data; Through the above technical solution, this example provides the adjusted processor resource allocation for the ath low-demand application at the yth time point The processor resource allocation after adjustment for the a-th high-demand application at the y-th time point , which can be respectively expressed by the formula With the formula Calculated, through this calculation method, combined with the processor resource demand index of the a-th application at the y-th time point in the current use process Since the data is obtained based on diversified data calculations, the accuracy of the data is relatively high. Allocating processor resources to different applications based on the data can improve the accuracy of the allocation results, thereby achieving optimized management of processor resources.

[0028] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A digital collaborative cloud computing resource optimization management system based on AI, characterized in that: The system comprises: A data recording module is used to record the data processing volume of different applications in the computer during historical use; A data collection module is used to collect the running status data of different applications when the computer is currently in use; The cloud computing module is used to combine the data collected in the data collection module with the data in the data recording module to calculate the processor resource demand index of different applications at different time points during the current use process; The AI ​​evaluation module is used to evaluate the processor resource requirements of different applications at that time point based on the calculation results in the data analysis module; The data decision module is used to make decisions on resource allocation for different applications based on the evaluation results of the data evaluation module; The resource allocation module is used to allocate processor resources to different applications based on the decision results of the data decision module and the calculation results in the data analysis module.

2. According to claim 1, the AI-based digital collaborative cloud computing resource optimization management system is characterized in that: The data collected in the data collection module includes: The memory usage, computing intensity, usage time and number of clicks of different applications on the computer.

3. According to claim 2, the AI-based digital collaborative cloud computing resource optimization management system is characterized in that: The computing process in the cloud computing module includes: By formula Calculate the data processing volume dispersion coefficient of the ath application in the past period of time ; Where a is any application in the computer, i is any use of the application, n is the number of times the application has been used in the past period of time, and , is the total data processing volume used by the a-th application for the i-th time in the past period of time, is the total number of minutes when the a-th application was used for the i-th time in the past period of time. For all The average value of For all The maximum value in For all The minimum value in is the proportionality coefficient, which is set based on empirical fitting.

4. According to claim 3, the AI-based digital collaborative cloud computing resource optimization management system is characterized in that: The computing process in the cloud computing module also includes: By calculating the discrete coefficient of the data processing volume of the a-th application in the past period of time Assign values ​​to generate a weighted impact value of the ath application that is between 1 and 5 and decreases as the discrete coefficient increases; Among them, the data processing volume dispersion coefficient of the ath application in the past period of time is The corresponding weight impact value of the a-th application is defined as .

5. According to claim 4, the AI-based digital collaborative cloud computing resource optimization management system is characterized in that: The computing process in the cloud computing module also includes: By formula Calculate the processor resource demand index of the ath application at the yth time point during the current use process ; Among them, y is the data collected at a fixed time interval during the current use process, is the total number of data collection times at the yth time point during the current use process, is the data processing volume of the ath application at the yth time point during the current use process, for The standard value of is the memory usage of the ath application at the yth time point during the current use. for The standard value of is the computational intensity influence coefficient of the a-th application, which is set based on empirical fitting. is the usage time of the ath application at the yth time point during the current usage process, is the total usage time of the computer at the yth time point, is the number of times the ath application is clicked at the yth time point during the current use. for The standard value of .

6. According to claim 5, the AI-based digital collaborative cloud computing resource optimization management system is characterized in that: The evaluation process of the AI ​​evaluation module includes: By calculating the processor resource demand index of the a-th application at the y-th time point in the current use process The preset processor resource demand index threshold range Make a comparison; like , the system determines that the application requires less processor resources; like , the system determines that the processor resources required by the application are medium; like , the system determines that the application requires more processor resources.

7. The AI-based digital collaborative cloud computing resource optimization management system according to claim 6 is characterized in that: The decision-making process of the data decision module includes: When it is determined that the application requires less processor resources, it is defined as a low-demand application, and based on the amount of processor resources currently occupied by the application, the amount of processor resources allocated is reduced; When it is determined that the processor resources required by the application are medium, the processor resource allocation amount is not adjusted; When it is determined that the application requires a large amount of processor resources, it is defined as a high-demand application, and the amount of processor resources allocated is increased based on the amount of processor resources currently occupied by the application.

8. The AI-based digital collaborative cloud computing resource optimization management system according to claim 7 is characterized in that: The adjustment process of the resource allocation module includes: When it is determined that the processor resources required by the a-th application are small; By formula Calculate the adjusted processor resource allocation for the ath low-demand application at the yth time point ; When it is determined that the a-th application requires more processor resources; By formula Calculate the adjusted processor resource allocation for the ath high-demand application at the yth time point ; in, The preset processor resource allocation for the ath low-demand application, The preset processor resource allocation for the a-th high-demand application, It is the adjustment coefficient comparison table function, because according to the empirical data The degree to which the range of values ​​affects the amount of processor resource allocation is obtained based on test data.

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