Application energy efficiency optimization method and device, server and storage medium

Abstract

The invention relates to the field of data processing, and discloses an energy efficiency optimization method and device of an application, a server and a storage medium. The method comprises the steps that performance data of a target application is obtained, data analysis operation is executed on the performance data, a data analysis result is obtained, and the data analysis result comprises a long-term performance trend, a performance index mutual relation and a performance anomaly potential reason; according to the data analysis result, generating an energy efficiency optimization strategy corresponding to the target application; and executing an energy efficiency optimization operation on the target application according to the energy efficiency optimization strategy. According to the embodiment of the invention, the risk of poor optimization effect caused by artificial experience deviation is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to an application energy efficiency optimization method, device, server and storage medium. Background Art

[0002] As the number of applications increases, the energy consumed by their operation is also increasing. Energy efficiency is an important consideration in the application operation process. Energy-efficient applications can not only reduce energy consumption and operating costs, but also improve system stability. However, in actual applications, many developers often face the problem of how to effectively optimize the energy efficiency of applications.

[0003] Traditional application energy efficiency optimization methods usually rely on the experience and intuition of developers. This approach often leads to limited optimization effects and may even cause new performance problems. A new technical means is needed to solve the above technical problems. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem that the energy efficiency optimization effect of the application is not good.

[0005] A first aspect of the present invention provides a method for optimizing energy efficiency of an application, the method comprising:

[0006] Acquire performance data of a target application, and perform data analysis operations on the performance data to obtain data analysis results, wherein the data analysis results include long-term performance trends, correlations between performance indicators, and potential causes of performance anomalies;

[0007] Generating an energy efficiency optimization strategy corresponding to the target application according to the data analysis results;

[0008] According to the energy efficiency optimization strategy, an energy efficiency optimization operation is performed on the target application.

[0009] Optionally, in a first implementation manner of the first aspect of the present invention, the step of generating an energy efficiency optimization strategy corresponding to the target application according to the data analysis result includes:

[0010] Generate a performance analysis report corresponding to the target application according to the data analysis results;

[0011] An energy efficiency optimization strategy corresponding to the target application is generated according to the performance analysis report.

[0012] Optionally, in a second implementation manner of the first aspect of the present invention, after the step of performing an energy efficiency optimization operation on the target application according to the energy efficiency optimization strategy, the method further includes:

[0013] Obtaining a performance comparison result before and after the energy efficiency optimization operation is executed, and, based on the comparison result, evaluating the implementation effect of the energy efficiency optimization strategy;

[0014] If the implementation effect does not meet the preset conditions, the energy efficiency optimization strategy is adjusted to obtain a target energy efficiency optimization strategy, and a secondary optimization operation is performed according to the target energy efficiency optimization strategy.

[0015] Optionally, in a third implementation manner of the first aspect of the present invention, the step of performing a data analysis operation on the performance data to obtain a data analysis result includes:

[0016] The performance data is input as an input parameter to a pre-trained data analysis model, wherein the data analysis model performs the data analysis operation according to the performance data to obtain the data analysis result.

[0017] Optionally, in a fourth implementation of the first aspect of the present invention, the data analysis includes trend analysis, correlation analysis and potential anomaly analysis.

[0018] Optionally, in a fifth implementation of the first aspect of the present invention, the step of performing an energy efficiency optimization operation according to the energy efficiency optimization strategy includes:

[0019] Determine implementation steps, required resources and time frame based on the stated energy efficiency optimization strategy;

[0020] The energy efficiency optimization operation is performed according to the implementation steps, required resources and time frame.

[0021] Optionally, in a sixth implementation manner of the first aspect of the present invention, the step of obtaining performance data of the target application includes:

[0022] The performance data of the target application is collected, and the performance data includes CPU usage, memory usage, disk I / O operations, and network traffic.

[0023] A second aspect of the present invention provides an application energy efficiency optimization device, comprising a memory and a processor, wherein the processor is used to execute program instructions stored in the memory so that the application energy efficiency optimization device performs the above-mentioned application energy efficiency optimization method.

[0024] A third aspect of the present invention provides a server, characterized in that it includes a memory and a processor, and the processor is used to execute program instructions stored in the memory so that the server executes the above-mentioned energy efficiency optimization method of the application.

[0025] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for optimizing the energy efficiency of an application.

[0026] In an embodiment of the present invention, the performance data of the target application is obtained, and a data analysis operation is performed on the performance data to obtain a data analysis result, wherein the data analysis result includes a long-term performance trend, a relationship between performance indicators, and potential causes of performance anomalies; based on the data analysis result, an energy efficiency optimization strategy corresponding to the target application is generated; and based on the energy efficiency optimization strategy, an energy efficiency optimization operation is performed on the target application. By performing a comprehensive data analysis on the performance data of the target application (including long-term performance trends, relationships between performance indicators, potential causes of performance anomalies, etc.), a highly targeted energy efficiency optimization strategy can be generated, thereby effectively reducing energy consumption and improving the energy utilization of the application. This method does not rely on the developer's experience and intuition, but automatically generates optimization strategies based on data analysis, reducing the risk of poor optimization results caused by manual experience bias. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of an embodiment of an energy efficiency optimization method applied in an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of a specific embodiment of step 102 of the energy efficiency optimization method used in an embodiment of the present invention;

[0029] Figure 3 It is a schematic diagram of a specific embodiment after step 103 of the energy efficiency optimization method applied in the embodiment of the present invention;

[0030] Figure 4 A schematic diagram of an embodiment of an energy efficiency optimization device used in an embodiment of the present invention;

[0031] Figure 5 FIG. 1 is a schematic diagram of an embodiment of a server in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] Embodiments of the present invention provide an application energy efficiency optimization method, device, server and storage medium.

[0033] The embodiments disclosed in the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments disclosed in the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments disclosed in the present invention are only for exemplary purposes and are not intended to limit the scope of protection disclosed in the present invention.

[0034] In the description of the embodiments disclosed in the present invention, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0035] The terms involved in the energy efficiency optimization methods applied include but are not limited to:

[0036] Applied Energy Efficiency Optimization Methods: Technical solutions to improve the energy efficiency of specific applications through data analysis and strategy formulation.

[0037] Performance data: Metrics about the running status of the target application, including multiple indicators such as CPU usage, memory usage, disk I / O operations, and network traffic.

[0038] Data analysis operations: Process and analyze the collected performance data to identify performance trends, correlations, and potential anomalies.

[0039] Data analysis results: Information obtained through data analysis operations, including long-term performance trends, correlations between performance indicators, and potential causes of performance anomalies.

[0040] Energy efficiency optimization strategy: A specific optimization plan based on data analysis results, aimed at improving the energy efficiency of the application.

[0041] Performance analysis report: A document generated based on data analysis results that describes in detail the performance status of the application and possible optimization measures.

[0042] Performance comparison results: The difference in performance data obtained before and after performing energy efficiency optimization operations is usually used to evaluate the effectiveness of the optimization strategy.

[0043] Preset conditions: Evaluation criteria set before the optimization strategy is implemented to determine whether the optimization effect meets expectations.

[0044] Secondary optimization operation: After the initial optimization operation, if the implementation effect is not ideal, the optimization strategy is adjusted and re-implemented according to the evaluation results.

[0045] Data analysis model: A model built using machine learning or statistical methods that processes performance data and outputs analytical results.

[0046] Trend analysis: Observe performance data over time to identify patterns and trends in performance changes.

[0047] Correlation analysis: Analyze the relationship between different performance indicators to find out the key factors that affect application efficiency.

[0048] Potential anomaly analysis: Identify and analyze anomalies in performance data so that timely corrective actions can be taken.

[0049] Implementation steps: Specific execution steps formulated according to the optimization strategy, used to guide the implementation process of the optimization operation.

[0050] Required resources: The hardware, software, or human resources required to perform the optimization operation.

[0051] Timeframe: The predetermined time frame during which the optimization operation is performed.

[0052] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 An embodiment of the energy efficiency optimization method used in the embodiment of the present invention includes:

[0053] 101. Acquire performance data of a target application, and perform a data analysis operation on the performance data to obtain data analysis results, wherein the data analysis results include long-term performance trends, correlations between performance indicators, and potential causes of performance anomalies;

[0054] Specifically, performance data of the target application is collected.

[0055] Pull application performance data regularly through the API interface. Perform data preprocessing on the collected performance data to ensure data quality.

[0056] Optionally, the data analysis includes trend analysis, correlation analysis and potential anomaly analysis. The main purpose of data analysis is to extract valuable information from the collected performance data to support the formulation of subsequent energy efficiency optimization strategies.

[0057] First, obtain the performance data of the target application. This data usually includes but is not limited to CPU usage, memory usage, disk I / O operations, and network traffic.

[0058] Feed the performance data into a pre-trained data analysis model. In this way, information can be extracted from the data more efficiently.

[0059] By analyzing time series data, we can identify the long-term trend of application performance. This can help us determine the performance of applications in different time periods and identify possible performance bottlenecks.

[0060] Analyze the correlation between various performance indicators. For example, the relationship between CPU usage and memory usage. Through these correlations, you can understand the mutual impact between different resources and help to manage resources more effectively.

[0061] Identify potential causes of performance anomalies, such as whether performance has degraded under high load. This information helps to identify and resolve issues in a timely manner, ensuring stable operation of the application. For analysis algorithms:

[0062] Trend analysis: Identify patterns in performance data over time, helping to uncover long-term trends and cyclical changes.

[0063] Correlation analysis: Study the relationship between different performance indicators to understand how each indicator affects each other.

[0064] Potential Anomaly Analysis: Detect outliers in performance data and identify data points that differ significantly from normal behavior so that timely action can be taken.

[0065] Optionally, the performance data is input as an input parameter into a pre-trained data analysis model, wherein the data analysis model performs the data analysis operation according to the performance data to obtain the data analysis result. The pre-processed performance data is input into a pre-trained data analysis model (such as a machine learning model). The model performs data analysis, extracts key features, and generates data analysis results.

[0066] The data analysis results include long-term performance trends (such as the changing trend of CPU usage), the relationship between performance indicators (such as the correlation between memory and CPU usage), and potential causes of performance anomalies (such as memory leaks under high load conditions).

[0067] Optionally, the performance data of the target application is collected, the performance data including CPU usage, memory usage, disk I / O operations and network traffic, wherein a monitoring tool (such as Prometheus, Grafana, etc.) is called to collect the performance data of the target application in real time.

[0068] 102. Generate an energy efficiency optimization strategy corresponding to the target application according to the data analysis result;

[0069] Specifically, based on the data analysis results, identify performance bottlenecks and anomalies, and formulate targeted optimization strategies, such as adjusting application configuration, optimizing algorithms, improving hardware resources, or performing load balancing.

[0070] 103. Perform an energy efficiency optimization operation on the target application according to the energy efficiency optimization strategy.

[0071] Specifically, according to the generated energy efficiency optimization strategy, the specific steps of the optimization operation are clarified.

[0072] Determine the required resources (e.g., computing resources, storage resources, etc.) and time frame (e.g., daily, weekly, etc.).

[0073] According to the implementation steps, the optimization operations are actually performed. For example, code optimization, configuration adjustment or hardware upgrade are performed.

[0074] Before and after the optimization operation is implemented, relevant performance data is collected, comparative analysis is conducted, and the optimization effect is evaluated.

[0075] If the evaluation results do not meet the preset conditions, adjust the strategy. According to the evaluation results, revise or re-formulate the target energy efficiency optimization strategy.

[0076] A secondary optimization operation is performed to further improve energy efficiency.

[0077] In a specific scenario, the energy efficiency optimization method of the application includes five steps: performance monitoring, data analysis, optimization strategy generation, strategy implementation, and effect evaluation and feedback. The performance monitoring module collects application performance data in real time, performs preprocessing and generates reports. The data analysis module processes monitoring data, identifies performance and energy usage patterns, and generates optimization suggestions. The optimization strategy generation module formulates optimization measures based on the analysis results, documents and publishes them after simulation evaluation. The strategy implementation module ensures the correct execution of the strategy, monitors the effect and adjusts it. The feedback and adjustment module evaluates the implementation effect, adjusts the strategy to adapt to changes, and forms a cycle of continuous improvement. The entire process ensures the continuous optimization of energy efficiency and performance of low-code platform applications.

[0078] In an embodiment of the present invention, the performance data of the target application is obtained, and a data analysis operation is performed on the performance data to obtain a data analysis result, wherein the data analysis result includes a long-term performance trend, a relationship between performance indicators, and potential causes of performance anomalies; based on the data analysis result, an energy efficiency optimization strategy corresponding to the target application is generated; and based on the energy efficiency optimization strategy, an energy efficiency optimization operation is performed on the target application. By performing a comprehensive data analysis on the performance data of the target application (including long-term performance trends, relationships between performance indicators, potential causes of performance anomalies, etc.), a highly targeted energy efficiency optimization strategy can be generated, thereby effectively reducing energy consumption and improving the energy utilization of the application. This method does not rely on the developer's experience and intuition, but automatically generates optimization strategies based on data analysis, reducing the risk of poor optimization results caused by manual experience bias.

[0079] Traditional performance monitoring and optimization methods often rely on manual experience, lack of systematicity and scientificity, resulting in a lack of integration of performance data from different systems and platforms, making it impossible to conduct comprehensive performance analysis. In order to solve the above technical problems, the present invention proposes an optional embodiment.

[0080] Reference Figure 2 , Figure 2 This is a schematic diagram of a specific embodiment of step 102 of the energy efficiency optimization method used in an embodiment of the present invention. Step 102 also includes the following specific implementation methods:

[0081] 1021. Generate a performance analysis report corresponding to the target application according to the data analysis result;

[0082] Specifically, the data analysis results obtained from claim 1 are summarized so that all important indicators (such as long-term performance trends, relationships between performance indicators, and potential abnormal causes) are included.

[0083] According to user needs, the performance analysis results are organized into a report format, including charts and text descriptions, to facilitate user understanding.

[0084] The report should include a detailed description of performance indicators, trend graphs, correlation analysis charts, and a detailed description and impact analysis of potential anomalies.

[0085] 1022. Generate an energy efficiency optimization strategy corresponding to the target application according to the performance analysis report.

[0086] Among them, key optimization directions and strategies are identified based on the findings in the performance analysis report.

[0087] Specific strategies include but are not limited to: Adjust resource allocation, such as increasing the allocation ratio of CPU or memory. Reconfigure application settings to improve efficiency (such as adjusting the cache mechanism). Select a better algorithm or optimize the code to reduce resource consumption.

[0088] The generated optimization strategy is recorded in detail, including implementation steps, expected results and possible risks. A clear implementation guide is provided to ensure the smooth progress of subsequent optimization operations.

[0089] In the embodiment of the present invention, by analyzing performance data and generating optimization strategies, the energy utilization efficiency of the application can be improved, resource waste can be reduced, and energy consumption costs can be reduced. By identifying and resolving potential performance anomalies, the implementation of the optimization strategy will improve the overall performance of the application and the stability of the system. Compared with empirical rules, the optimization strategy based on data analysis provides a more scientific and reliable basis and reduces the risk of optimization decisions.

[0090] Traditional energy efficiency optimization methods are mostly static solutions, which are difficult to optimize in real time according to changes in application performance. When performing performance analysis, existing systems are often unable to effectively integrate data from multiple sources, resulting in insufficient overall control over application performance. In order to solve the above technical problems, the present invention proposes an optional embodiment.

[0091] Reference Figure 3 , Figure 3 This is a schematic diagram of a specific embodiment after step 103 of the energy efficiency optimization method applied in the embodiment of the present invention. Step 103 also includes the following specific implementation methods:

[0092] 201. Obtaining a performance comparison result before and after the energy efficiency optimization operation is executed, and, based on the comparison result, evaluating the implementation effect of the energy efficiency optimization strategy;

[0093] Specifically, before performing the optimization operation, performance data of the target application is collected and baseline performance indicators (such as CPU usage, memory usage, disk I / O operations, and network traffic) are recorded.

[0094] According to the previously generated energy efficiency optimization strategy, specific optimization operations are implemented, including but not limited to resource adjustment, application parameter changes, etc.

[0095] After the optimization operation is performed, repeat the collection of the same type of performance data and record the optimized performance indicators.

[0096] Compare the performance data before and after optimization, and use statistical methods (such as t-test, analysis of variance, etc.) for quantitative analysis to evaluate the optimization effect.

[0097] 202. If the implementation effect does not meet the preset conditions, adjust the energy efficiency optimization strategy to obtain a target energy efficiency optimization strategy, and perform a secondary optimization operation according to the target energy efficiency optimization strategy.

[0098] Specifically, based on the comparison results, an effect evaluation report is generated, including: comparison of key performance indicators before and after optimization. Whether the preset performance improvement conditions are met (such as whether the improvement percentage reaches a certain threshold).

[0099] Analysis and recommendations for unmet conditions.

[0100] Analyze the reasons and identify potential factors that affect the optimization effect (such as improper optimization steps or resource allocation). Based on the performance evaluation results, adjust the original energy efficiency optimization strategy to form a new target strategy. According to the adjusted target energy efficiency optimization strategy, implement secondary optimization operations and repeat the above steps to achieve better performance results.

[0101] In this optional embodiment, through real-time performance comparison and evaluation, the optimization process has the ability to dynamically adjust, and can respond quickly according to actual conditions to ensure continuous improvement of system performance. Relying on quantitative performance comparison analysis, managers can make scientific decisions based on data.

[0102] As data centers and enterprises continue to increase their demand for computing resources, how to effectively utilize these resources to reduce energy consumption and operating costs has become a focus of technological development. However, existing energy management methods often lack systematicity and cannot be accurately optimized for specific applications. As a result, many applications lack optimization strategies during operation, further leading to a waste of energy resources. In order to solve the above technical problems, the present invention proposes an optional embodiment.

[0103] Step 103 also includes the following specific implementations:

[0104] 1031. Determine the implementation steps, required resources and time frame based on the energy efficiency optimization strategy;

[0105] Specifically, based on the generated energy efficiency optimization strategy, determine the specific implementation steps. For example, optimization tasks for specific applications (such as reducing unnecessary service calls). Specifically, evaluate the resources and time required to implement the strategy. For example, some optimizations may require additional computing resources or need to be performed during off-peak hours.

[0106] 1032. Perform the energy efficiency optimization operation according to the implementation steps, required resources and time frame.

[0107] After determining the implementation steps, perform optimization operations according to the established plan, including adjusting system parameters and reconfiguring services or application instances.

[0108] In this optional embodiment, through the systematic optimization strategy, energy consumption can be effectively reduced, thereby improving overall energy efficiency. Not only does it reduce operating costs, but it also reduces the impact on the environment. After rationally allocating resources, the performance of the application is improved to a certain extent, saving energy while meeting business needs.

[0109] Reference Figure 4 , the embodiment of the present disclosure provides an energy efficiency optimization device 400 for an application, including an energy efficiency optimization device body for an application, and an energy efficiency optimization device 405 for an application. The energy efficiency optimization device 405 for an application includes a processor (processor) 404 and a memory (memory) 401 storing program instructions. Optionally, the device may also include a communication interface (Communication Interface) 402 and a bus 403. Among them, the processor 404, the communication interface 402, and the memory 401 can complete mutual communication through the bus 403. The communication interface 402 can be used for information transmission. The processor 404 can call the program instructions in the memory 401 to execute the energy efficiency optimization method of the application of the above embodiment. In addition, the logical instructions in the above-mentioned memory 401 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. The memory 401, as a computer-readable storage medium, can be used to store software programs, computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 404 executes the functional application and data processing by running the program instructions / modules stored in the memory 401, that is, the energy efficiency optimization method applied in the above embodiment is realized. The memory 401 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 401 may include a high-speed random access memory and may also include a non-volatile memory.

[0110] Reference Figure 5 , the embodiment of the present disclosure provides a server 500, including a processor (processor) 504 and a memory (memory) 501 storing program instructions. Optionally, the server may also include a communication interface (CommunicationInterface) 502 and a bus 503. Among them, the processor 504, the communication interface 502, and the memory 501 can communicate with each other through the bus 503. The communication interface 502 can be used for information transmission. The processor 504 can call the program instructions in the memory 501 to execute the energy efficiency optimization method of the application of the above embodiment.

[0111] In addition, the logic instructions in the above-mentioned memory 501 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. The memory 501, as a computer-readable storage medium, can be used to store software programs, computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 504 executes functional applications and data processing by running the program instructions / modules stored in the memory 501, that is, the energy efficiency optimization method applied in the above-mentioned embodiment is implemented. The memory 501 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 501 may include a high-speed random access memory and may also include a non-volatile memory.

[0112] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes, or a transient storage medium.

[0113] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0114] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0115] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0116] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for optimizing energy efficiency of an application, characterized in that: The energy efficiency optimization method of the application includes: Acquire performance data of a target application, and perform data analysis operations on the performance data to obtain data analysis results, wherein the data analysis results include long-term performance trends, correlations between performance indicators, and potential causes of performance anomalies; Generating an energy efficiency optimization strategy corresponding to the target application according to the data analysis results; According to the energy efficiency optimization strategy, an energy efficiency optimization operation is performed on the target application.

2. The energy efficiency optimization method of application according to claim 1, characterized in that: The step of generating an energy efficiency optimization strategy corresponding to the target application according to the data analysis result includes: Generate a performance analysis report corresponding to the target application according to the data analysis results; An energy efficiency optimization strategy corresponding to the target application is generated according to the performance analysis report.

3. The energy efficiency optimization method of application according to claim 1, characterized in that: After the step of performing the energy efficiency optimization operation on the target application according to the energy efficiency optimization strategy, the method further includes: Obtaining a performance comparison result before and after the energy efficiency optimization operation is executed, and, based on the comparison result, evaluating the implementation effect of the energy efficiency optimization strategy; If the implementation effect does not meet the preset conditions, the energy efficiency optimization strategy is adjusted to obtain a target energy efficiency optimization strategy, and a secondary optimization operation is performed according to the target energy efficiency optimization strategy.

4. The energy efficiency optimization method of application according to claim 1, characterized in that: The step of performing a data analysis operation on the performance data to obtain a data analysis result comprises: The performance data is input as an input parameter to a pre-trained data analysis model, wherein the data analysis model performs the data analysis operation according to the performance data to obtain the data analysis result.

5. The energy efficiency optimization method of application according to claim 4, characterized in that: The data analysis includes trend analysis, correlation analysis and potential anomaly analysis.

6. The method for optimizing energy efficiency of an application according to claim 1, characterized in that: The step of performing the energy efficiency optimization operation according to the energy efficiency optimization strategy comprises: Determine implementation steps, required resources and time frame based on the stated energy efficiency optimization strategy; The energy efficiency optimization operation is performed according to the implementation steps, required resources and time frame.

7. The method for optimizing energy efficiency of an application according to claim 1, characterized in that: The step of obtaining the performance data of the target application includes: The performance data of the target application is collected, and the performance data includes CPU usage, memory usage, disk I / O operations, and network traffic.

8. An energy efficiency optimization device for application, characterized in that, It comprises a memory and a processor, wherein the processor is used to execute program instructions stored in the memory to implement the energy efficiency optimization method of the application described in any one of claims 1 to 7.

9. A server, characterized in that: It comprises a memory and a processor, wherein the processor is used to execute program instructions stored in the memory to implement the energy efficiency optimization method of the application described in any one of claims 1 to 7.

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

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