A method and system for compatibility and adaptation of domestic IT applications based on cloud computing.

Abstract

This invention discloses a cloud computing-based method and system for compatibility and adaptation of domestic IT applications. This invention relates to the field of resource management technology, solving the technical problem of lacking refined means to quantify and effectively manage cross-application impacts and flexibly allocate resources to ensure the efficient, stable, and economical operation of the overall system. This invention deeply mines historical data of domestic IT applications, meticulously analyzes resource utilization rates for each time period, and combines scientifically set and dynamically calibrated preset values ​​to accurately identify high-frequency / low-frequency usage periods, peak / valley periods, and high / low utilization periods within valleys. This enables "on-demand supply" of cloud computing resources, ensuring smooth operation during peak periods and preventing waste during valleys, improving resource utilization, reducing unnecessary costs, tapping the potential of idle resources, laying a solid foundation for subsequent peak operation, enhancing the system's sustainable operation and maintenance capabilities, and achieving a closed-loop, efficient operation and maintenance management system across all time periods and scenarios.

Description

Technical Field

[0001] This invention relates to the field of resource management technology, specifically to a method and system for compatibility and adaptation of cloud computing-based domestic IT applications. Background Technology

[0002] With the vigorous development of the information technology application innovation (IT innovation) industry, numerous IT innovation applications built on domestic software and hardware systems are emerging and widely deployed in cloud computing environments. Under the complex architecture of cloud computing with multi-tenancy and elastic resource allocation, IT innovation applications face diverse operating conditions and resource competition scenarios.

[0003] According to CN118444979B, a method and system for compatibility adaptation of cloud computing-based information technology applications is provided. This method involves clustering all search feature arrays of the target generation layer to obtain multiple sets of search feature arrays. The target generation layer then performs calculations based on each set of search feature arrays and the log text feature arrays corresponding to the target software's runtime logs, resulting in generated search feature arrays corresponding to each search feature array in each set. By using sets as the smallest unit for calculations, the number of search feature arrays processed by the target generation layer in each operation is reduced. As the number of processed search feature arrays decreases, the processing difficulty of the target generation layer decreases, the processing speed increases, and the anomaly detection speed is improved.

[0004] However, under the complex architecture of cloud computing's multi-tenancy and elastic resource allocation, domestically developed applications face diverse operating conditions and resource competition scenarios. On the one hand, business load varies significantly at different times, with both busy peak periods and relatively quiet off-peak periods. Traditional "one-size-fits-all" resource allocation and operation and maintenance management models are difficult to adapt to this dynamic change, easily leading to resource shortages and performance bottlenecks affecting user experience during peak periods, while idle resources waste costs during off-peak periods. On the other hand, domestically developed applications often coexist with many other collaborative applications on the cloud platform. These applications are intertwined and interconnected. During peak periods, resource contention among domestically developed applications may interfere with the normal operation of other applications. Furthermore, there is a lack of refined methods to quantify, effectively manage, and flexibly allocate resources to ensure the efficient, stable, and economical operation of the overall system. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a cloud computing-based method and system for compatibility and adaptation of domestically developed applications. This solves the problem of lacking sophisticated means to quantify and effectively manage cross-application impacts and flexibly allocate resources to ensure the efficient, stable, and economical operation of the overall system.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for compatibility and adaptation of domestically developed information technology applications based on cloud computing, which specifically includes the following steps:

[0007] Step S1: Obtain basic information and corresponding historical data of the domestic IT application;

[0008] Step S2: Analyze the usage periods of the information technology application based on the obtained historical data to obtain peak period information and off-peak period information;

[0009] Step S3: Analyze the information corresponding to the information during peak hours, judge the response time of other applications, and combine the basic information of other applications to generate allocation information.

[0010] Step S4: Analyze the information on information innovation corresponding to the off-peak period, and perform secondary classification based on the resource usage of the current period to obtain different utilization periods. At the same time, combine the distribution of different utilization periods to obtain allocation information.

[0011] As a further aspect of the present invention, the specific method for analyzing the usage period in step S2 is as follows:

[0012] Acquire historical data and simultaneously acquire the usage periods corresponding to the information technology application in the historical data. Compare the resource utilization rate with the preset value. If the resource utilization rate is greater than the preset value, the corresponding usage period is classified as a high-frequency usage period. Conversely, if the resource utilization rate is less than the preset value, the corresponding usage period is classified as a low-frequency usage period.

[0013] Similarly, all usage periods within a day are categorized, and high-frequency and low-frequency usage periods are combined separately to obtain peak and off-peak periods, while generating peak and off-peak period information.

[0014] As a further aspect of the present invention, the specific method for analyzing the information technology application corresponding to the peak period information in step S3 is as follows:

[0015] The response time of other applications during peak hours is recorded as T1, and the response time of other applications during normal hours is recorded as T2. The values ​​of response time T1 and response time T2 are compared. If response time T1 is less than response time T2, it means that the peak period has affected other applications, and the corresponding application is marked as an associated application. Conversely, if response time T1 is less than response time T2, it means that the peak period has not affected other applications, and the corresponding application is marked as an unassociated application. The unassociated application is then analyzed.

[0016] As a further aspect of the present invention, the specific method for analyzing non-related applications in step S3 is as follows:

[0017] Get all associated applications and label them as i, where i = 1, 2, ..., j, and j represents the number of associated applications. At the same time, get the resource utilization rate corresponding to associated application i and label it as Pi. Sort the resource utilization rate Pi from largest to smallest. Then get the usage time period corresponding to associated application i and classify the usage time period into working time period and non-working time period.

[0018] Obtain the current time period and determine the usage status of associated application i within the current time period. If the usage status of associated application i is during working hours, then mark the corresponding associated application i as a working application. Otherwise, if the usage period of associated application i is during non-working hours, then mark the corresponding associated application i as a non-working application. For the non-working applications obtained from the classification, generate shutdown adjustment information, and at the same time analyze the working applications.

[0019] As a further aspect of the present invention, the specific method for analyzing the working application in step S3 is as follows:

[0020] Let 'a' represent all categories of work applications, where a = 1, 2, ..., b, and b represents the quantity number corresponding to the work application. Then, retrieve the usage records of work application 'a' for the current time period from historical data. Simultaneously, retrieve the usage count (Ca) and usage duration (Ya) for work application 'a' from the usage records, and substitute these parameters into the formula. The optimal value Qa corresponding to work application a is calculated, where Za represents the resource utilization rate of work application a in the current time period. Similarly, the optimal values ​​Qa of all work applications a are calculated and sorted from largest to smallest according to the optimal values ​​Qa.

[0021] Next, obtain the resource utilization rate corresponding to the domestic IT application in the current time period, denoted as Z1. At the same time, obtain the overall remaining resource utilization rate, and according to the remaining resource utilization rate, shut down the adaptation of the working application a in descending order of the preferred value to generate allocation information.

[0022] As a further aspect of the present invention, the specific method for analyzing the information technology application during off-peak hours in step S4 is as follows:

[0023] The system obtains information on resource usage for IT innovation applications during off-peak hours and compares these usages. If resource usage exceeds a preset value, the corresponding time period is classified as a high-utilization period; conversely, if resource usage is less than the preset value, the corresponding time period is classified as a low-utilization period. The system also performs allocation analysis on high-utilization periods.

[0024] As a further aspect of the present invention, the specific method for performing allocation analysis on high-utilization periods in step S4 is as follows:

[0025] All high-utilization periods are obtained, and it is determined whether the high-utilization periods are consecutive periods. Continuous high-utilization periods and non-continuous high-utilization periods are generated. For continuous high-utilization periods, they are combined to obtain high-utilization intervals. Similarly, the processing method for peak periods is used to generate allocation information.

[0026] For non-continuous high utilization periods, the time interval between two high utilization periods is obtained and compared with a preset time value. If the time interval is greater than the preset time value, the non-continuous high utilization periods are not combined. If the time interval is less than the preset time value, the non-continuous high utilization periods are combined. The same processing method is used for peak periods to generate allocation information.

[0027] A cloud computing-based domestic IT application compatibility and adaptation system includes: an information acquisition unit, an adaptive analysis unit, a peak period analysis unit, an off-peak period analysis unit, and a dispatch information output unit;

[0028] The information acquisition unit is used to acquire basic information of the information technology application and transmit the basic information to the adaptive analysis unit.

[0029] The adaptive analysis unit is used to analyze the acquired basic information and classify the usage time of the information technology application by combining historical data to obtain peak time information and off-peak time information. Then, the peak time information is transmitted to the peak time analysis unit and the off-peak time information is transmitted to the off-peak time analysis unit.

[0030] The peak period analysis unit analyzes the acquired peak period information. By judging the response time of other applications corresponding to the peak period, the other applications are classified into related applications and unrelated applications. Based on the usage time of related applications, the units are further classified into working periods and unworking periods. The units are further classified into working applications and unworking applications based on the current usage status of related applications. The units calculate the preferred value for working applications, generate allocation information based on the preferred value, and transmit the allocation information to the allocation information output unit.

[0031] The off-peak period analysis unit is used to analyze the information innovation applications corresponding to the off-peak period, and to perform secondary classification based on the resource usage of the current period to obtain different utilization periods. At the same time, it combines the distribution of different utilization periods to obtain allocation information, and then transmits the allocation information to the allocation information output unit.

[0032] The allocation information output unit is used to display the acquired allocation information to the corresponding operators.

[0033] This invention provides a method and system for compatibility and adaptation of domestically developed information technology applications based on cloud computing. Compared with existing technologies, it has the following advantages:

[0034] This invention deeply mines historical data of information technology application, analyzes the resource utilization rate of each time period in detail, and combines scientifically set and dynamically calibrated preset values ​​to accurately identify high-frequency / low-frequency usage periods, peak / low-frequency periods, and high / low utilization periods in low-frequency periods. In this way, cloud computing resources are "supplyed on demand", ensuring no lag during peak periods and no waste during low-frequency periods, improving resource utilization and reducing unnecessary cost expenditures.

[0035] During peak hours, by rigorously comparing the response times of other applications at different times and using comprehensive basic information, we can accurately mark related applications, sort and analyze working / non-working applications by multiple dimensions such as resource utilization and usage time, and reasonably shut down and adapt non-critical working applications by using optimization value calculations to reduce the "side effects" of domestic innovation applications, stabilize the services of other applications, strengthen overall coordination and system robustness, and reduce performance fluctuations and failure risks caused by peak impacts.

[0036] Based on a detailed classification of resources during off-peak hours, and for continuous / non-continuous high-utilization periods, scientific judgment logic (such as based on time intervals and preset values) is set to flexibly combine and allocate resources, tap the potential of idle resources, and arrange tasks such as data maintenance and system optimization. This not only improves the resource turnover rate during off-peak hours, but also lays a solid foundation for subsequent peak operation and enhances the system's sustainable operation and maintenance capabilities, achieving a closed loop of efficient operation and maintenance management across all time periods and scenarios. Attached Figure Description

[0037] Figure 1 This is a diagram illustrating the steps and methods of the present invention;

[0038] Figure 2 This is a block diagram illustrating the system principle of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1, please refer to Figure 1 This application provides a method for compatibility adaptation of domestically developed information technology applications based on cloud computing. The method specifically includes the following steps:

[0041] Step S1: Obtain the basic information and corresponding historical data of the domestic IT application. The basic information obtained here includes the resource utilization rate and response time of the domestic IT application, and the historical data specifically refers to the historical working data of the domestic IT application.

[0042] Step S2: Analyze the usage periods of the information technology application based on the obtained historical data to obtain peak period information and off-peak period information.

[0043] Historical data is acquired, and the usage time periods corresponding to the domestic IT application in the historical data are also acquired. The usage time period here is the duration of a unit of time. The usage time period is classified into high-frequency usage time period and low-frequency usage time period according to the basic information of the domestic IT application corresponding to the usage time period. The main criterion for this classification is to analyze the resource utilization rate of the domestic IT application and compare the resource utilization rate with the preset value. If the resource utilization rate is greater than the preset value, the corresponding usage time period is classified as a high-frequency usage time period. Conversely, if the resource utilization rate is less than the preset value, the corresponding usage time period is classified as a low-frequency usage time period.

[0044] For example, within a certain hourly interval, the average CPU utilization of this government office application is 60%, the average memory utilization is 70%, and the average disk I / O bandwidth utilization is 30MB / s (assuming the server's full disk I / O load is 100MB / s, the bandwidth utilization is 30%). Using the formula above, we can calculate: Resource utilization = 60% × 40% + 70% × 40% + 30% × 20% = 24% + 28% + 6% = 58%. The preset resource utilization threshold is 50% (this threshold can be set based on past experience, application performance baselines, or industry standards, and should be periodically calibrated according to business development). When the calculated resource utilization within a unit of time in a certain hour is greater than 50%, that period is classified as a high-frequency usage period; conversely, if it is less than 50%, it is classified as a low-frequency usage period.

[0045] Similarly, all usage periods within a day are categorized, and high-frequency and low-frequency usage periods are combined separately to obtain peak and off-peak periods, while generating peak and off-peak period information.

[0046] Step S3: Analyze the information corresponding to the information during peak hours, judge the response time of other applications, and combine the basic information of other applications to generate allocation information.

[0047] The response time of other applications during peak hours is denoted as T1. Specifically, "other applications" here refers to applications that are currently in operation. At the same time, the response time of other applications during normal hours is denoted as T2. The values ​​of response time T1 and response time T2 are compared. If response time T1 is less than response time T2, it means that the peak hours have affected other applications, and the corresponding application is marked as an associated application. Conversely, if response time T1 is less than response time T2, it means that the peak hours have not affected other applications, and the corresponding application is marked as an unassociated application.

[0048] Get all associated applications and label them as i, where i = 1, 2, ..., j, and j represents the number of associated applications. At the same time, get the resource utilization rate corresponding to associated application i and label it as Pi. Sort the resource utilization rates Pi from largest to smallest. Then, get the usage time period corresponding to associated application i, where the usage time period is a unit time period, specifically one hour. Classify the usage time period into working time period and non-working time period, and define the classification according to the corresponding resource utilization rate. If the resource utilization rate is greater than the threshold, the threshold standard is determined based on the resource utilization rate under working conditions, and is the average value.

[0049] Get the current time period and determine the usage status of the associated application i within the current time period. If the usage status of the associated application i is during working hours, then mark the corresponding associated application i as a working application. Otherwise, if the usage period of the associated application i is during non-working hours, then mark the corresponding associated application i as a non-working application. For the non-working applications obtained from the classification, generate the shutdown adjustment information.

[0050] Further analysis of work applications is conducted, obtaining all categorized work applications denoted as 'a', where a = 1, 2, ..., b, and b represents the quantity label corresponding to the work application. Next, the usage records of work application 'a' corresponding to the current time period are obtained from historical data. Simultaneously, the number of uses of work application 'a' in the usage records is denoted as Ca, and the usage duration is denoted as Ya, where the usage duration Ya is the average duration of a single use. These parameters are then substituted into the formula. The optimal value Qa corresponding to work application a is calculated, where Za represents the resource utilization rate of work application a in the current time period. Similarly, the optimal values ​​Qa of all work applications a are calculated and sorted from largest to smallest according to the optimal values ​​Qa.

[0051] Next, obtain the resource utilization rate corresponding to the domestic IT application in the current time period, denoted as Z1. At the same time, obtain the overall remaining resource utilization rate, and according to the remaining resource utilization rate, shut down the adaptation of the working application a in descending order of the preferred value to generate allocation information.

[0052] Example 2, as Example 2 of the present invention, is implemented based on Example 1, and differs from Example 1 in the following ways:

[0053] Step S4: Analyze the information on information innovation corresponding to the off-peak period, and perform secondary classification based on the resource usage of the current period to obtain different utilization periods. At the same time, combine the distribution of different utilization periods to obtain allocation information.

[0054] The system obtains information on resource usage during off-peak hours for domestic IT applications and compares these usages. If resource usage exceeds a preset value (which represents the peak performance threshold within the off-peak period, set by the operator), the corresponding period is classified as a high-utilization period. Conversely, if resource usage is below the preset value, it is classified as a low-utilization period. Reviewing 30 days of historical data, the average CPU utilization during off-peak hours is 8%, with a standard deviation of 2%. The preset value, calculated as 1.5 times the standard deviation, is 8% + 1.5 × 2% = 11%. The average memory usage is 2.5GB, with a standard deviation of 0.5GB. The preset value is 2.5GB + 1.5 × 0.5GB = 3.25GB. The average disk I / O read / write speed is 8MB / s, with a standard deviation of 1MB / s. The preset value is 8MB / s + 1.5 × 1MB / s = 9.5MB / s. Compared with the data of the current period, the CPU utilization rate is 12% > 11%, the memory usage is 3GB < 3.25GB, and the disk I / O read / write speed is 10MB / s > 9.5MB / s. Because the CPU and disk I / O exceed the preset values, the period from 2:00 AM to 3:00 AM is determined to be a high utilization period. At the same time, the high utilization period and low utilization period are adjusted and analyzed.

[0055] The allocation analysis is performed on high utilization periods to obtain all high utilization periods and determine whether the high utilization periods are consecutive periods to generate consecutive high utilization periods and non-consecutive high utilization periods. The criteria for determining consecutive periods are whether they are adjacent periods. If they are adjacent periods, they are consecutive periods; if they are not adjacent periods, they are classified as non-consecutive periods. For consecutive high utilization periods, they are combined to obtain high utilization intervals. Similarly, the allocation information is generated by processing peak periods.

[0056] For non-continuous high utilization periods, the time interval between two high utilization periods is obtained and compared with a preset time value. The specific value of the preset time value is set by the operator. If the time interval is greater than the preset time value, the non-continuous high utilization periods are not combined. If the time interval is less than the preset time value, the non-continuous high utilization periods are combined. Similarly, the peak period processing method is used to generate allocation information.

[0057] The specific methods for allocating and analyzing low-utilization periods are the same as those for high-utilization periods.

[0058] Example 3, as Example 3 of the present invention, focuses on combining the implementation processes of Example 1 and Example 2.

[0059] Example 4, please refer to Figure 2 This application provides a cloud computing-based domestic IT application compatibility and adaptation system, including: an information acquisition unit, an adaptive analysis unit, a peak period analysis unit, an off-peak period analysis unit, and a dispatch information output unit, and in conjunction with the attached... Figure 2 It can be seen that the above functional units are connected electrically in one direction.

[0060] The information acquisition unit is used to acquire basic information of the information technology application and transmit the basic information to the adaptive analysis unit.

[0061] An adaptive analysis unit is used to analyze the acquired basic information and classify the usage time of the information technology application by combining historical data to obtain peak time information and off-peak time information. Then, the peak time information is transmitted to the peak time analysis unit and the off-peak time information is transmitted to the off-peak time analysis unit. The specific method of classifying the usage time here is the same as the processing procedure in step S2 of embodiment one.

[0062] The peak period analysis unit analyzes the acquired peak period information. By judging the response time of other applications corresponding to the peak period, the other applications are classified into related applications and non-related applications. Based on the usage time of related applications, the units are further classified into working periods and non-working periods. The units are further classified into working applications and non-working applications based on the current usage status of related applications. The units calculate the preferred value corresponding to the working applications and generate allocation information based on the preferred value. The allocation information is then transmitted to the allocation information output unit. The specific method of generating allocation information here is the same as the processing procedure in step S3 of embodiment one.

[0063] The off-peak period analysis unit is used to analyze the information innovation applications corresponding to the off-peak period information, and to perform secondary classification based on the resource usage of the current period to obtain different utilization periods. At the same time, it combines the distribution of different utilization periods to obtain allocation information, and then transmits the allocation information to the allocation information output unit. The processing here is the same as the processing process in step S4 of embodiment 2.

[0064] The allocation information output unit is used to display the acquired allocation information to the corresponding operators.

[0065] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0066] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for compatibility and adaptation of domestically developed information technology applications based on cloud computing, characterized in that, The method specifically includes the following steps: Step S1: Obtain basic information and corresponding historical data of the domestic IT application; Step S2: Analyze the usage periods of the information technology application based on the obtained historical data to obtain peak period information and off-peak period information; Step S3 involves analyzing the information from peak periods for domestic IT applications, judging the response time of other applications, and combining this with the basic information of the other applications to generate allocation information. The specific processing method is as follows: Let 'a' represent all categories of work applications, where a = 1, 2, ..., b, and b represents the quantity number corresponding to the work application. Then, retrieve the usage records of work application 'a' for the current time period from historical data. Simultaneously, retrieve the usage count (Ca) and usage duration (Ya) for work application 'a' from the usage records, and substitute these parameters into the formula. The optimal value Qa corresponding to work application a is calculated, where Za represents the resource utilization rate of work application a in the current time period. Similarly, the optimal values ​​Qa of all work applications a are calculated and sorted from largest to smallest according to the optimal values ​​Qa. Next, obtain the resource utilization rate corresponding to the domestic IT application in the current time period, denoted as Z1, and at the same time obtain the overall remaining resource utilization rate. Based on the remaining resource utilization rate, the working application a is turned off in descending order of the preferred value to generate allocation information. Step S4: Analyze the information on information innovation corresponding to the off-peak period, and perform secondary classification based on the resource usage of the current period to obtain different utilization periods. At the same time, combine the distribution of different utilization periods to obtain allocation information. The specific method for analyzing the application of information technology innovation during off-peak hours in step S4 is as follows: The system obtains the resource usage of information technology innovation applications during off-peak hours and compares the resource usage. If the resource usage is greater than the preset resource value, the corresponding time period is classified as a high-utilization time period. Conversely, if the resource usage is less than the preset resource value, the corresponding time period is classified as a low-utilization time period. At the same time, the system performs allocation analysis on the high-utilization time period. The specific method for performing allocation analysis during high-utilization periods in step S4 is as follows: All high-utilization periods are obtained, and it is determined whether the high-utilization periods are consecutive periods. Continuous high-utilization periods and non-continuous high-utilization periods are generated. For continuous high-utilization periods, they are combined to obtain high-utilization intervals. Similarly, the processing method for peak periods is used to generate allocation information. For non-continuous high utilization periods, the time interval between two high utilization periods is obtained and compared with a preset time value. If the time interval is greater than the preset time value, the non-continuous high utilization periods are not combined. If the time interval is less than the preset time value, the non-continuous high utilization periods are combined. The same processing method is used for peak periods to generate allocation information.

2. The method for compatibility and adaptation of cloud computing-based domestic IT applications according to claim 1, characterized in that, The specific method for analyzing the usage period in step S2 is as follows: Acquire historical data and simultaneously acquire the usage periods corresponding to the information technology application in the historical data. Compare the resource utilization rate with the preset value. If the resource utilization rate is greater than the preset value, the corresponding usage period is classified as a high-frequency usage period. Conversely, if the resource utilization rate is less than the preset value, the corresponding usage period is classified as a low-frequency usage period. Similarly, all usage periods within a day are categorized, and high-frequency and low-frequency usage periods are combined separately to obtain peak and off-peak periods, while generating peak and off-peak period information.

3. The method for compatibility and adaptation of cloud computing-based information technology application as described in claim 1, characterized in that, The specific method for analyzing the information-based IT application corresponding to peak time periods in step S3 is as follows: The response time of other applications during peak hours is recorded as T1, and the response time of other applications during normal hours is recorded as T2. The values ​​of response time T1 and response time T2 are compared. If response time T1 is less than response time T2, it means that the peak period has affected other applications, and the corresponding application is marked as an associated application. Conversely, if response time T1 is less than response time T2, it means that the peak period has not affected other applications, and the corresponding application is marked as an unassociated application. The unassociated application is then analyzed.

4. The method for compatibility and adaptation of cloud computing-based information technology application as described in claim 3, characterized in that, The specific method for analyzing non-related applications in step S3 is as follows: Get all associated applications and label them as i, where i = 1, 2, ..., j, and j represents the number of associated applications. At the same time, get the resource utilization rate corresponding to associated application i and label it as Pi. Sort the resource utilization rate Pi from largest to smallest. Then get the usage time period corresponding to associated application i and classify the usage time period into working time period and non-working time period. Obtain the current time period and determine the usage status of associated application i within the current time period. If the usage status of associated application i is during working hours, then mark the corresponding associated application i as a working application. Otherwise, if the usage period of associated application i is during non-working hours, then mark the corresponding associated application i as a non-working application. For the non-working applications obtained from the classification, generate shutdown adjustment information, and at the same time analyze the working applications.

5. A cloud computing-based domestic IT application compatibility adaptation system, used to execute the cloud computing-based domestic IT application compatibility adaptation method according to any one of claims 1-4, characterized in that, include: Information acquisition unit, adaptive analysis unit, peak period analysis unit, off-peak period analysis unit, and allocation information output unit; The information acquisition unit is used to acquire basic information of the information technology application and transmit the basic information to the adaptive analysis unit. The adaptive analysis unit is used to analyze the acquired basic information and classify the usage time of the information technology application by combining historical data to obtain peak time information and off-peak time information. Then, the peak time information is transmitted to the peak time analysis unit and the off-peak time information is transmitted to the off-peak time analysis unit. The peak period analysis unit analyzes the acquired peak period information. By judging the response time of other applications corresponding to the peak period, the other applications are classified into related applications and unrelated applications. Based on the usage time of related applications, the units are further classified into working periods and unworking periods. The units are further classified into working applications and unworking applications based on the current usage status of related applications. The units calculate the preferred value for working applications, generate allocation information based on the preferred value, and transmit the allocation information to the allocation information output unit. The off-peak period analysis unit is used to analyze the information innovation applications corresponding to the off-peak period, and to perform secondary classification based on the resource usage of the current period to obtain different utilization periods. At the same time, it combines the distribution of different utilization periods to obtain allocation information, and then transmits the allocation information to the allocation information output unit. The allocation information output unit is used to display the acquired allocation information to the corresponding operators.

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