Method, apparatus, storage medium and electronic device for adjusting configuration of a storage system

Abstract

An embodiment of the present application provides a method, apparatus, computer-readable storage medium, and electronic device for adjusting the configuration of a storage system. The method includes: obtaining the current configuration of the storage system; determining the working performance parameters of the storage system when processing a target service model according to the current configuration; determining the risk indicators of the storage system when it is in the working performance parameters according to the current configuration of the storage system and the working performance parameters; and adjusting the current configuration of the storage system when the risk indicators are greater than a preset threshold, so that the risk indicators corresponding to the adjusted current configuration are less than or equal to the preset threshold. Through the closed-loop feedback mechanism and configuration adjustment, the system can dynamically and adaptively optimize its own configuration to cope with changing service models and potential failure risks. Thus, in different scenarios, the storage system can maintain stable performance and high availability, improving the reliability and business continuity of the storage system.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of computers, and more particularly, to a method and apparatus for adjusting the configuration of a storage system, a computer-readable storage medium, and an electronic device. Background Art

[0002] With the development of new technologies such as big data, cloud computing, and artificial intelligence, there are increasingly high requirements for data processing and storage capabilities. In modern data-intensive application environments, storage systems need to provide high-performance and highly stable storage services for enterprises or data centers to meet the business scenarios of enterprises or data centers.

[0003] In related technologies, to ensure the performance of a storage system, the storage system is usually regularly inspected and the configuration of the storage system is adjusted regularly according to the requirements of data processing.

[0004] However, in the face of the current increasingly diverse and complex data processing requirements, adjusting in the manner of related technologies will result in insufficient stability and availability of the storage system. Summary of the Invention

[0005] Embodiments of the present application provide a method and apparatus for adjusting the configuration of a storage system, a computer-readable storage medium, and an electronic device, which can monitor the storage system in real time and dynamically adjust the configuration of the storage system to ensure the stability and availability of the storage system.

[0006] According to an embodiment of the present application, a method for adjusting the configuration of a storage system is provided. The method includes: obtaining the current configuration of the storage system, where the configuration includes a configuration combination and configuration parameters; determining the working performance parameters of the storage system when processing a target business model according to the current configuration; determining a risk index of the storage system when in the working performance parameters according to the current configuration of the storage system and the working performance parameters; and adjusting the current configuration of the storage system when the risk index is greater than a preset threshold, so that the risk index corresponding to the adjusted current configuration is less than or equal to the preset threshold.

[0007] In an exemplary embodiment, determining a risk index of the storage system when in the working performance parameters according to the current configuration of the storage system and the working performance parameters includes: respectively simulating the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios, where the configuration of the simulated storage system is the current configuration and the performance parameters of the simulated storage system are the working performance parameters; and determining the risk index according to the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios.

[0008] In an exemplary embodiment, the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios is simulated respectively, including: determining the potential fault conditions of the storage system according to the types of configurations of the storage system; constructing a variety of fault scenarios according to the potential fault conditions, wherein each fault scenario corresponds to a potential fault condition; inputting the storage system and the variety of fault scenarios into a preset analysis model to simulate the performance of the storage system in the variety of fault scenarios respectively.

[0009] In an exemplary embodiment, the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios is simulated respectively, and it further includes: determining the potential load conditions of the storage system according to a variety of service models corresponding to the storage system; constructing a variety of load scenarios according to the potential load conditions, wherein each load scenario corresponds to a potential load condition; inputting the storage system and the variety of load scenarios into a preset analysis model to simulate the performance of the storage system in the variety of load scenarios respectively.

[0010] In an exemplary embodiment, risk metrics are determined according to the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios, including: determining a fault risk metric according to the performance of the storage system in the variety of different fault scenarios; determining a stress risk metric according to the performance of the storage system in the variety of different load scenarios; determining a risk metric according to the fault risk metric and the stress risk metric.

[0011] In an exemplary embodiment, when the risk metric is greater than a preset threshold, the current configuration of the storage system is adjusted so that the risk metric corresponding to the adjusted current configuration is less than or equal to the preset threshold, including: when the risk metric is greater than the preset threshold, inputting the current configuration of the storage system and a target service model into the analysis model to determine a target configuration combination and target configuration parameters that make the risk metric less than or equal to the preset threshold when the storage system processes the target service model; adjusting the current configuration of the storage system according to the target configuration combination and the target configuration parameters.

[0012] In an exemplary embodiment, when the risk indicator is greater than a preset threshold, the current configuration of the storage system and the target business model are input into an analysis model to determine a target configuration combination and target configuration parameters that enable the risk indicator to be less than or equal to the preset threshold when the storage system processes the target business model, including: inputting the current configuration of the storage system and the target business model into the analysis model; obtaining various adjusted configuration situations of the storage system fed back by the analysis model, where each adjusted configuration situation includes an adjusted configuration combination and configuration parameters; determining the risk indicator of the storage system in each adjusted configuration situation; and taking an adjusted configuration situation with the corresponding risk indicator less than or equal to the preset threshold as the target adjusted configuration situation, where the adjusted configuration situation includes the target configuration combination and target configuration parameters.

[0013] In an exemplary embodiment, according to the current configuration situation, the working performance parameters of the storage system when processing the target business model are determined, including: inputting the current configuration situation of the storage system and the target business model into a preset prediction model, where the prediction model is trained using a training sample set, and the training sample set includes historical performance parameters of the storage system when processing different business models under different configurations; and determining the working performance parameters of the storage system when processing the target business model according to the output result of the prediction model.

[0014] In an exemplary embodiment, the method further includes: obtaining the actual working performance parameters of the storage system when processing the target business model under the current configuration; determining the error situation of the prediction model according to the actual working performance parameters and the predicted working performance parameters determined based on the output result of the prediction model; and adjusting the weight parameters of the prediction model according to the error situation.

[0015] In an exemplary embodiment, obtaining the current configuration situation of the storage system includes: determining target configuration metrics related to the performance parameters of the storage system; obtaining the configuration parameters corresponding to the target configuration metrics of the storage system; and obtaining the current configuration situation of the storage system according to the configuration parameters corresponding to the target configuration metrics.

[0016] In an exemplary embodiment, when the risk indicator is greater than a preset threshold, after adjusting the current configuration situation of the storage system so that the risk indicator corresponding to the adjusted current configuration situation is less than or equal to the preset threshold, the method further includes: obtaining the actual working performance parameters of the storage system in real time; and when the actual working performance parameters exceed a preset range, adjusting the current configuration situation of the storage system so that the actual working performance parameters are within the preset range.

[0017] Another aspect of the present application provides an adjustment device for the configuration of a storage system, the device comprising: a configuration situation acquisition module for acquiring the current configuration situation of the storage system; a working performance parameter determination module for determining, according to the current configuration situation, the working performance parameters of the storage system when processing a target service model; a risk index determination module for determining, according to the current configuration situation and the working performance parameters of the storage system, the risk index of the storage system when in the working performance parameters; and an adjustment module for adjusting the current configuration situation of the storage system when the risk index is greater than a preset threshold, so that the risk index corresponding to the adjusted current configuration situation is less than or equal to the preset threshold.

[0018] According to another embodiment of the present application, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0019] According to another embodiment of the present application, there is also provided an electronic device, comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0020] According to another embodiment of the present application, there is also provided a computer program product, the computer program product comprising a computer program, and the computer program realizes the steps in any one of the above method embodiments when executed by a processor.

[0021] The above adjustment method for the configuration of the storage system of the present application predicts its working performance parameters when processing a target service model by acquiring the current configuration situation of the storage system, and analyzes the risk index in combination with the fault scenario and the load scenario. The evaluation of the risk index can help the system identify potential problems and performance bottlenecks, provide a quantitative basis for subsequent configuration adjustment, and ensure that the system can respond in a timely manner and maintain stable performance output in complex business and fault scenarios. Thus, it is possible to evaluate the rationality and security of the configuration before the system runs, avoid performance bottlenecks or fault risks caused by improper configuration, and improve the stability and efficiency of the storage system. In summary, through the closed-loop feedback mechanism and configuration adjustment, the system can dynamically optimize its own configuration to cope with changing service models and potential fault risks. In this way, even in high-load or fault scenarios, the storage system can maintain stable performance and high availability, improving the reliability and business continuity of the storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0023] Figure 1 It is a hardware structure block diagram of a server device for a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0024] Figure 2 It is a flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0025] Figure 3 It is the second flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0026] Figure 4 It is the third flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0027] Figure 5 It is the fourth flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0028] Figure 6 It is the fifth flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0029] Figure 7 It is the sixth flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0030] Figure 8 It is the seventh flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0031] Figure 9 It is the eighth flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0032] Figure 10 It is the ninth flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0033] Figure 11 It is the tenth flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0034] Figure 12 It is the eleventh flowchart of a method of adjusting the configuration of a storage system according to an embodiment of the present application;

[0035] Figure 13 It is a structure block diagram of an adjustment device for the configuration of a storage system according to an embodiment of the present application;

[0036] Figure 14 It is a schematic structural diagram of an optional electronic device according to an embodiment of the present application. Detailed implementation manners

[0037] In the following, embodiments of the present application will be described in detail with reference to the accompanying drawings and in conjunction with the embodiments.

[0038] It should be noted that the terms "first", "second", etc. in the description and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0039] The embodiment of the method for adjusting the configuration of the storage system provided in the embodiment of the present application can be executed in a server device or a similar computing device. Taking the operation on the server device as an example, Figure 1 is a hardware structure block diagram of the server device of a method for adjusting the configuration of a storage system according to an embodiment of the present application. As Figure 1 shown, the server device may include one or more ( Figure 1 only one is shown in the figure) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microcontroller unit (MCU) or a field programmable gate array (FPGA)) and a memory 104 for storing data. Among them, the above-mentioned server device may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above-mentioned server device. For example, the server device may further include more or fewer components than Figure 1 shown in the figure, or have a different configuration from Figure 1 shown in the figure.

[0040] The memory 104 can be used to store computer programs. For example, software programs and modules of application software, such as the computer program corresponding to the method for adjusting the configuration of the storage system in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above-mentioned method is implemented. The memory 104 may include a high-speed random access memory, and may further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely provided with respect to the processor 102, and these remote memories can be connected to the server device through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an enterprise internal network, a local area network, a mobile communication network, and their combinations.

[0041] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the server device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0042] As described in the background technology, the storage systems in the related art may have insufficient performance problems in the face of the increasingly diverse and complex data processing needs. The inventors have found that the management strategies of the storage systems in the related art are often based on preset configuration files. After these configurations are set at the initial stage of system deployment, they are usually not adjusted according to real-time business needs or system status. During business peak periods or when resources are tight, this static configuration may cause performance bottlenecks, affecting data processing speed and response time. In addition, although some storage systems allow administrators to manually adjust configurations to deal with performance issues, this manual optimization method is difficult to respond to rapidly changing business needs in real time, and has high requirements on the skills and experience of administrators. In complex or emergency situations, manual adjustments may not be timely or accurate, resulting in reduced stability of the storage system.

[0043] In view of the above problems, a method for adjusting the configuration of a storage system is provided in this embodiment, which can monitor and dynamically adjust the configuration of the storage system in real time to ensure the stability and availability of the storage system. Figure 2 As shown, the method includes the following steps S200-230:

[0044] Step S200, obtaining the current configuration of the storage system.

[0045] Among them, the configuration includes configuration combinations and configuration parameters. A configuration combination refers to the combination method of multiple configuration items in a storage system, reflecting the overall strategy of the system when dealing with different business scenarios. A configuration combination may include processor core binding policies, memory allocation, Redundant Arrays of Independent Disks (RAID) settings, the enabling status of advanced features (such as data deduplication and compression), etc. The combination method of these configuration items determines how the storage system allocates hardware resources, processes data reading and writing, and manages advanced features, thus affecting the overall performance and system stability. For example, the processor core binding policy determines the usage method of the Central Processing Unit (CPU) cores in the system. For instance, specific processes or tasks are bound to specific CPU cores to optimize task processing speed and resource utilization. Memory configuration includes memory allocation policies, cache size, data page replacement algorithms, etc., which affect the data reading speed and system response time. RAID configurations, such as RAID 0, RAID 5, RAID 6, etc., the selection and setting of different RAID levels affect data redundancy, read and write performance, and storage efficiency. The status of advanced features affects whether the storage system enables functions such as data deduplication, compression, and encryption, as well as the configuration parameters of these functions, which have a significant impact on system resource occupancy and performance. Configuration parameters refer to the specific numerical values or status settings of each configuration item in the configuration combination. For example, in the processor core binding policy, the configuration parameter may be which cores are specified for processing which process. In memory configuration, the parameters may include the cache size, the priority of the replacement policy, etc. In RAID configuration, the parameters may involve stripe size, data block size, whether to enable a hot spare disk, etc.; in advanced features, the parameters may involve the ratios of deduplication and compression, the selection of encryption algorithms, etc. Configuration parameters are the key to optimizing the performance of the storage system. Reasonably set parameters can significantly improve the system's operation efficiency and data processing ability, while incorrect or inappropriate parameter settings may lead to performance degradation, resource waste, or data security issues.

[0046] Specifically, the current configuration of the storage system can be obtained in real-time or periodically. The configuration includes, but is not limited to, processor core binding policies, memory configuration, Redundant Arrays of Independent Disks (RAID) stripe width, the enabling status of volume type advanced features, etc., laying a foundation for subsequent performance prediction and analysis.

[0047] Exemplarily, the storage system can be monitored continuously, and the current configuration parameters can be accessed through system calls or monitoring APIs. These parameters may be stored in the system configuration file or may be dynamically reflected in the operating status of the storage system. The acquisition module will regularly read this information and format it into the input data format required for subsequent processing (such as performance prediction).

[0048] Step S210, determine the working performance parameters of the storage system when processing the target business model according to the current configuration.

[0049] Specifically, analyze the expected performance metrics of the storage system when processing the target business model under the current configuration, such as the number of read and write operations per second (Input / Output Operations Per Second, IOPS), bandwidth, latency, etc., to provide a basis for optimizing the system configuration.

[0050] Exemplarily, the performance prediction module can be used to receive the current configuration parameters and target business model data (such as concurrency, access mode, etc.), and predict the performance of the storage system under these conditions through a pre-trained Recurrent Neural Network (RNN) model. The RNN model can learn the relationship between the configuration parameters and performance metrics based on historical data, so as to provide a prediction of the future operating performance of the storage system. Through the performance prediction of the RNN, the system can understand in advance the expected performance of processing specific services under specific configurations, which helps to take preventive measures before the system reaches the performance bottleneck or fails, improving the availability and efficiency of the system.

[0051] Step S220, determine the risk metrics of the storage system when it is in the working performance parameters according to the current configuration of the storage system and the working performance parameters.

[0052] Specifically, after predicting the working performance parameters of the storage system, further analyze the currently stored configuration, the reliability when in this working performance parameter, and evaluate the risks that the storage system may encounter when processing this service, such as performance degradation, failure probability, etc.

[0053] Exemplarily, the deep Q-learning algorithm can be used to simulate the performance of the storage system under different failure scenarios (such as controller failure, IO (Input / Output) card failure, etc.) and stress scenarios (such as business peak periods). By analyzing the predicted performance parameters and evaluating the response capabilities of the simulated scenarios, the risk metrics of the system when processing specific services are calculated. The evaluation of the risk metrics can help the system identify potential problems and performance bottlenecks, provide a quantitative basis for subsequent configuration adjustments, and ensure that the system can respond in a timely manner and maintain stable performance output under complex services and failure scenarios.

[0054] Step S230: When the risk indicator is greater than the preset threshold, adjust the current configuration of the storage system so that the risk indicator corresponding to the adjusted current configuration is less than or equal to the preset threshold.

[0055] Specifically, if the risk indicator exceeds the preset safety or performance threshold, the system can automatically adjust the configuration to reduce risks and improve performance.

[0056] Exemplarily, when detecting that the risk indicator is too high, the configuration parameters of the storage system can be automatically adjusted based on the optimization strategy provided by the deep Q network (DQN) model. The system can adjust the processor core binding strategy, optimize memory allocation, adjust the RAID stripe width, etc. In addition, the configuration action grading list ensures that the adjustment process will not have too much impact on the business, and high-level adjustments are executed through secondary authorization by the administrator when needed. Through the closed-loop feedback mechanism and configuration adjustment, the system can dynamically optimize its own configuration to cope with changing business models and potential failure risks. In this way, even in high-load or failure scenarios, the storage system can maintain stable performance and high availability, improving the reliability and business continuity of the entire system.

[0057] In this embodiment, by obtaining the current configuration of the storage system, predicting its working performance parameters when processing the target business model, and analyzing the risk indicator in combination with the failure scenario and load scenario, the evaluation of the risk indicator can help the system identify potential problems and performance bottlenecks, provide a quantitative basis for subsequent configuration adjustment, and ensure that the system can respond in a timely manner and maintain stable performance output in complex business and failure scenarios. Thus, the rationality and security of the configuration can be evaluated before the system runs, avoiding performance bottlenecks or failure risks caused by improper configuration, and improving the stability and efficiency of the storage system. In summary, through the closed-loop feedback mechanism and configuration adjustment, the system can dynamically optimize its own configuration to cope with changing business models and potential failure risks. In this way, even in high-load or failure scenarios, the storage system can maintain stable performance and high availability, improving the reliability and business continuity of the storage system.

[0058] In one embodiment, when adjusting the current configuration of the storage system, it is necessary to query the preset configuration action grading list. In the configuration action grading list, multiple configuration items are graded according to the impact degree of the configuration on the business. Actions of high-level configurations require secondary authorization by the administrator, and actions of low-level configurations can be directly run automatically. This list can be adjusted according to the actual situation.

[0059] Specifically, configuring an action grading list classifies the configuration adjustment operations in the system according to the magnitude of their impact on the business, so as to guide the system on how to select appropriate configuration adjustment strategies in different situations. Specifically, an action grading list can be embedded in the system, and each configuration item in the list details the configuration action and its evaluation level of impact on the business. For example, adjusting the processor core binding policy may be listed as a high-level action because it directly involves the allocation of the system's computing power and has a greater impact on the immediate response of the business. Adjusting the RAID stripe width or memory cache policy may be listed as a medium-level action because they affect the read / write efficiency and latency of data, and their impact on the business is relatively small but still significant. Enabling or disabling data compression and deduplication functions can be listed as low-level actions because they mainly affect data storage efficiency and long-term space utilization, and have little impact on immediate business.

[0060] Specifically, to meet the requirements in different business models and system states, an adjustment interface for the action grading list can be designed to allow system administrators to adjust the action grading list according to the actual situation. The administrator can adjust the grading of the configuration actions. For example, during a specific business peak period, the processor core binding policy may be adjusted to a low-level action, allowing the closed-loop feedback correction module to automatically optimize under high load to ensure business continuity. During non-peak periods, this action can be adjusted to a high level, requiring secondary authorization to avoid unnecessary resource waste and performance fluctuations.

[0061] In this embodiment, through the action grading list, the system can perform configuration adjustments more intelligently and cautiously. High-level actions can be designed to require secondary authorization from the administrator, ensuring that there is a decision-making process with human intervention before making adjustments that may have a significant impact on the business, reducing the risk of business interruption caused by automatic adjustment. The low-level action module can run automatically, which means that the system can automatically perform configuration adjustments that slightly improve performance without affecting business stability, improving the system's adaptability and operation and maintenance efficiency. By adjusting the action grading list, the system can more flexibly respond to the performance optimization requirements in different business models and system states. This mechanism not only improves the system's adaptability but also ensures the controllability of business risks during the performance optimization process, reduces operation and maintenance costs, enhances business continuity and user experience. It provides system administrators with sufficient control rights, enabling them to precisely guide system optimization strategies on the premise of ensuring normal business operation, so as to achieve the best performance and resource utilization in their respective business scenarios.

[0062] In one embodiment, as Figure 3As shown in the figure, in step S220, according to the current configuration of the storage system and the working performance parameters, determine the risk index of the storage system when it is at the working performance parameters. It includes steps S300 - S310:

[0063] In step S300, respectively simulate the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios.

[0064] Specifically, when the configuration of the simulated storage system is the current configuration and the performance parameters of the simulated storage system are the working performance parameters, respectively simulate the working states of the storage system in different types of faults and high - load situations, and evaluate the system performance.

[0065] Exemplarily, a deep Q - learning algorithm can be used to simulate a variety of fault scenarios (such as controller failure, IO card failure, etc.) and load scenarios (such as business peak period, batch data processing, etc.). Through the DQN model, the system can explore various state - action combinations and find out the performance performance and possible performance bottlenecks of the system in specific scenarios.

[0066] In step S310, determine the risk index according to the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios.

[0067] Specifically, based on the performance of the storage system in the simulation, quantify the risks that the storage system may encounter under a specific business model, such as the magnitude of performance degradation, the probability of failure, etc.

[0068] Exemplarily, the simulation results can be analyzed, and the degree of performance degradation, the frequency of failures, and the fluctuations of other performance indicators of the storage system in different scenarios can be used as risk indicators. The determination of risk indicators may involve statistical analysis and weighted average to comprehensively evaluate the risk levels in different scenarios.

[0069] In this embodiment, by simulating the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios, it is possible to simulate and evaluate the performance of the storage system under various conditions, improve the accuracy of prediction and the efficiency of fault scenario analysis, and provide a solid foundation for the subsequent determination of risk indicators. By simulating different scenarios, the system can comprehensively evaluate its response capabilities in the face of faults and high loads, helping to identify potential performance problems and risk points in advance, providing data support for system optimization and fault prevention, enhancing the self - adaptability and stability of the system. The quantification of risk indicators helps system administrators and automated management systems intuitively understand the vulnerability of the storage system when facing different challenges, provides a quantitative basis for configuration adjustment and optimization, ensures the high reliability and availability of the system in complex scenarios, reduces the risk of business interruption, and improves the overall operation and maintenance efficiency.

[0070] In one embodiment, as Figure 4 shown, in step S300, the performance of the storage system is respectively simulated in a variety of different fault scenarios and a variety of different load scenarios. This includes steps S400 - S420:

[0071] Step S400, determine the potential fault conditions of the storage system according to the configuration types of the storage system.

[0072] Specifically, the possible fault types that may occur in the storage system can be pre - identified. These faults may originate from different configuration types, such as processors, memory, storage architectures, etc., and then a list of potential fault conditions is listed.

[0073] Exemplarily, by analyzing the current configuration types, including but not limited to processor core binding policies, memory configurations, RAID stripe widths, advanced function enablement status, etc., combined with fault history data and domain knowledge, a list containing a variety of potential fault conditions is constructed. For example, for the processor core binding policy, possible fault conditions include processor overheating, uneven core load, etc.; for memory configuration, possible fault conditions include memory leaks, insufficient memory, etc.

[0074] Step S410, construct a variety of fault scenarios according to the potential fault conditions.

[0075] Among them, each fault scenario corresponds to a potential fault condition.

[0076] Specifically, based on the identified potential fault conditions, a series of fault scenarios are constructed, and each scenario simulates the impact of a specific fault condition on the system performance.

[0077] Exemplarily, according to the list of potential fault conditions, a deep Q - learning algorithm is used to construct a variety of fault scenarios. This may involve setting specific simulation parameters for each fault condition, such as the frequency of fault occurrence, duration, scope of influence, etc., as well as changes in the business model under the fault condition (such as an increase in concurrency, a change in access pattern, etc.). The constructed fault scenarios need to be as close to the actual situation as possible so that the simulation results can reflect the risks in the real environment.

[0078] Step S420, input the storage system and a variety of fault scenarios into a preset analysis model to respectively simulate the performance of the storage system in a variety of fault scenarios.

[0079] Specifically, after constructing the storage system and the fault scenarios, these are input into the preset analysis model, and the performance changes of the storage system under different fault scenarios are evaluated through simulation.

[0080] Exemplarily, the analysis model can be based on the reinforcement learning algorithm of DQN, which receives the current configuration and working performance parameters of the storage system, as well as the corresponding fault scenario parameters. The model analyzes the changes in system performance indicators in case of faults, such as a decrease in IOPS and an increase in latency, by simulating the introduction of fault scenarios. The simulation process may involve multiple runs of the storage system, with different fault parameters introduced each time, to comprehensively evaluate the performance impact in different situations.

[0081] In this embodiment, by systematically identifying potential fault situations, the risk points of the storage system can be more comprehensively evaluated, providing a clear direction for subsequent construction and simulation of fault scenarios, and enhancing the system's fault warning and response capabilities. By constructing and simulating specific fault scenarios, the system can predict the changes in its performance in different fault situations, which helps to identify possible performance bottlenecks and fault points in advance, providing a basis for fault prevention and adaptive configuration adjustment, and enhancing the stability and reliability of the storage system. By simulating the performance of the storage system in fault scenarios, the system can quantify the impact degree of different faults on performance, providing data support for subsequent determination of risk indicators, helping the system to adopt targeted configuration adjustment strategies to ensure stable and reliable operation when potential faults occur, and improving the adaptability and availability of the storage system in a dynamic environment.

[0082] In one embodiment, as Figure 5 shown, in step S310, the performance of the storage system is respectively simulated in a variety of different fault scenarios and a variety of different load scenarios. It includes: steps S500 - S520:

[0083] Step S500, determine the potential load situation of the storage system according to a variety of service models corresponding to the storage system.

[0084] Specifically, based on a variety of service models supported by the storage system, predict the possible load types and intensities to prepare for the subsequent construction of load scenarios.

[0085] Exemplarily, the performance data acquisition module analyzes historical data and service characteristics according to the service models actually run by the storage system, such as database scenarios, virtualization scenarios, cloud platform scenarios, etc., to determine the potential service load situation. For example, for the database service model, possible potential loads include high-frequency random read and write, sudden increase in data volume, etc.; for the virtualization scenario, potential loads may involve multi-task concurrency, virtual machine migration, etc.

[0086] Step S510, construct a variety of load scenarios according to the potential load situation.

[0087] Among them, each load scenario corresponds to a potential load condition. Based on the determined potential load conditions, a series of load scenarios are constructed, and each scenario simulates the load conditions under a specific business model for subsequent performance simulation.

[0088] Exemplarily, according to the list of potential load conditions, multiple load scenarios are designed and constructed. This involves setting specific business model parameters, such as the number of concurrent requests, data block size, read / write ratio, etc., as well as the duration and frequency of simulation, to ensure that the load scenarios can truly reflect the potential business pressure. The construction of each scenario should simulate the actual business environment as much as possible to improve the reliability and accuracy of the simulation results.

[0089] Step S520, input the storage system and multiple load scenarios into a preset analysis model to simulate the performance of the storage system in multiple load scenarios respectively.

[0090] Specifically, after constructing the load scenarios, input the storage system and the scenarios into a preset analysis model, and evaluate the performance of the storage system when facing different business loads through simulation.

[0091] Exemplarily, the preset analysis models, namely the RNN model embedded in the performance prediction module and the DQN model used by the fault and stress scenario traversal analysis module, respectively simulate the changes in performance metrics of the system under these scenarios, such as IOPS, bandwidth, latency, etc., based on the current configuration and working performance parameters of the storage system, as well as the parameters of multiple load scenarios. The simulation may involve multiple runs of the storage system, with each run taking different load scenarios as inputs to comprehensively evaluate the performance changes of the system under different load conditions. Among them, the fault and stress scenario traversal analysis module, through the deep reinforcement learning algorithm, simulates different fault / stress / function scenarios (such as controller failure, IO card failure, high-potential stress scenarios with increased concurrency, function scenarios such as system upgrade / business migration, etc.) on the basis of predicting performance, analyzes the reliability of the system under such high-load or fault conditions, and identifies the performance bottleneck factor points under the current configuration.

[0092] In this embodiment, by identifying and predicting potential business load conditions, the behavior of the storage system under these loads can be simulated more accurately, which helps to identify possible performance bottlenecks and optimization points in advance, and improves the system's resilience and resource allocation efficiency. By building and simulating load scenarios, the system can estimate changes in its performance under different business pressures, which helps to identify possible performance bottlenecks and optimization requirements, and provide strategic guidance for the stable operation of the storage system during business peaks or other high-load scenarios, thereby enhancing the system's adaptability. By simulating the performance of the storage system under load scenarios, the system can quantify the impact of different business loads on performance, and provide data support for subsequent risk indicator determination and configuration optimization. This method helps to ensure that the storage system can still maintain efficient and stable services when facing business peaks or other high-load situations, reduces performance bottlenecks and avoids the risk of service interruptions, and improves overall system availability and business continuity.

[0093] In one embodiment, Figure 6 As shown, step S320 determines the risk index according to the performance of the storage system in various failure scenarios and various load scenarios. It includes: steps S600-S620:

[0094] Step S600: determining a failure risk index according to the performance of the storage system in a variety of different failure scenarios.

[0095] Specifically, by analyzing the performance degradation of the storage system under simulated failure scenarios, the performance of the system when facing a failure, namely the failure risk index, is quantified.

[0096] For example, the output data of the storage system under different failure scenarios can be recorded, such as IOPS drop, latency increase, throughput reduction, etc. The module evaluates the degree of performance degradation of the system when encountering a specific failure through statistical analysis, such as calculating the average, maximum or percentage of performance degradation, so as to determine the failure risk index.

[0097] Step S610: determining a pressure risk index according to the performance of the storage system in a variety of different load scenarios.

[0098] Specifically, by analyzing the system's performance under simulated high-load scenarios, the risks it faces during business peaks, namely the stress risk index, are quantified.

[0099] Exemplarily, the predicted data of the storage system under different load scenarios can be recorded, including performance metrics such as IOPS, bandwidth, latency, etc. By comparing the performance prediction values with the performance baseline of the system under low load, the degree of performance degradation can be calculated, which is used as the stress risk indicator. It may also be necessary to consider the resource utilization of the system, such as CPU and memory usage, to ensure the comprehensiveness of the risk indicator.

[0100] Step S620, determine the risk indicator according to the fault risk indicator and the stress risk indicator.

[0101] Specifically, the fault risk indicator and the stress risk indicator are combined to determine a comprehensive risk indicator for comprehensively evaluating the risks faced by the storage system.

[0102] Exemplarily, according to the quantization results of the fault risk indicator and the stress risk indicator, a comprehensive risk indicator is determined through certain mathematical methods or algorithms (such as weighted average, risk matrix analysis, etc.). This comprehensive indicator can be a numerical value or a list of risk levels, which is used to represent the overall risk level of the system when running under a specific business model.

[0103] In this embodiment, by determining the fault risk indicator, the system can identify in which fault scenarios the performance may be severely affected, which helps to take preventive measures, such as adjusting the configuration in advance and optimizing resource allocation, so as to reduce the performance loss when actual faults occur and improve the stability and reliability of the system. The determination of the stress risk indicator helps the system administrator to understand to what extent the system performance may decline in peak business or other high-load scenarios, so as to take corresponding resource optimization and load balancing strategies to ensure that the system can maintain good performance under any load and improve the service quality and user experience of the system. By comprehensively considering the fault risk and the stress risk, the system can more comprehensively identify potential performance bottlenecks and optimization requirements. The comprehensive risk indicator provides more comprehensive guidance for the formulation of configuration adjustment and fault prevention strategies, ensures the stable operation of the system in a complex environment, and improves the overall performance and availability of the storage system. In addition, the quantization and comprehensive analysis of different risk indicators can also help the system administrator or decision-maker to prioritize the most risky configurations or business models, optimize resource allocation and maintenance plans.

[0104] In one embodiment, as Figure 7 shown, step S230, when the risk indicator is greater than the preset threshold, adjust the current configuration of the storage system so that the risk indicator corresponding to the adjusted current configuration is less than or equal to the preset threshold. It includes: steps S700 - S710:

[0105] Step S700, when the risk indicator is greater than the preset threshold, input the current configuration of the storage system and the target business model into the analysis model to determine the target configuration combination and target configuration parameters that can make the risk indicator less than or equal to the preset threshold when the storage system processes the target business model.

[0106] Specifically, when the evaluated risk indicator exceeds the preset security threshold, it indicates that the current configuration of the storage system cannot effectively cope with potential failures or high loads, and new configuration combinations need to be found to reduce the risk.

[0107] Exemplarily, input the current configuration of the storage system (such as processor binding policy, memory configuration, RAID stripe width, etc.) and the target business model (such as database access mode, virtualization concurrency, etc.) into the analysis model (a deep Q-network (DQN)-based reinforcement learning algorithm). The analysis model evaluates the performance and risk under each configuration combination by simulating and traversing different configuration combinations, and finds the configuration combination and parameters that can reduce the risk indicator below the preset threshold.

[0108] Step S710, adjust the current configuration of the storage system according to the target configuration combination and target configuration parameters.

[0109] Specifically, after determining the target configuration combination and parameters, the system needs to perform configuration adjustment to update the current configuration to the target configuration to reduce the risk and improve the performance.

[0110] Exemplarily, based on the target configuration combination and parameters, specific configuration adjustment instructions can be generated, involving the adjustment of the processor core binding policy, the optimization of memory allocation, the change of RAID configuration, etc. These instructions can be executed automatically (if the configuration action level is low and no administrator authorization is required) or submitted to the system administrator for secondary confirmation (if the configuration action level is high and may affect the business). Once confirmed, the storage system will update its configuration to meet the requirements of the target configuration combination and parameters.

[0111] Exemplarily, in the traversal of fault and stress scenarios, simulation and analysis are performed in different scenarios through a deep Q-network (DQN) model. DQN learns the optimal strategy under different state and action combinations in reinforcement learning, continuously optimizes the system configuration to cope with fault or stress scenarios, and ensures the stable performance of the system under business requirements. The core formula of the DQN algorithm is as follows:

[0112]

[0113] Among them, the Q value = Q(s,a), and the Q value represents the expected total return of the system after taking action a in a certain state s. The state s here represents the current system configuration and business model, such as processor core binding, memory configuration, background advanced tasks, etc.; the action a represents the adjustment of a certain configuration item or the introduction of a fault under the current configuration, such as the controller fault, IO card fault, high-potential pressure scenario of increased concurrency, system upgrade / business migration and other functional scenarios mentioned in this application. k is the offset under hardware differences and can be adjusted through the parameter module. maxQ(s',a') represents the maximum expected total return of the system after taking action a' in the next state s'. That is to say, after taking the current action, when the system enters the next state s', in the next state s', all possible actions a' are executed, and then the maximum expected total return among the expected total returns obtained by executing all possible actions a' is used as maxQ(s',a'). Other learning rate α, immediate reward r, and discount factor γ are preset values. Through the iterative update of DQN, the system can select the optimal configuration under different fault or pressure scenarios to ensure the high reliability of the storage system.

[0114] In this embodiment, through the determination of the target configuration combination and parameters, the system can actively adjust the configuration, avoid performance degradation and stability problems in high-risk situations, ensure that the storage system can maintain the best performance state under different business models, and improve the availability and user experience of the system. The automatic configuration adjustment process improves the response speed and adaptability of the system, reduces the need for manual intervention, and lowers the operation and maintenance costs. And by adjusting the configuration to the best state, the system can effectively cope with potential faults and high loads, avoid performance degradation and business interruption, and ensure the high availability and business continuity of the storage system.

[0115] In one embodiment, as Figure 8 shown, in step S700, when the risk index is greater than the preset threshold, the current configuration of the storage system and the target business model are input into the analysis model to determine the target configuration combination and target configuration parameters that make the risk index of the storage system less than or equal to the preset threshold when processing the target business model. It includes: steps S800 - S830:

[0116] Step S800, input the current configuration of the storage system and the target business model into the analysis model.

[0117] Specifically, in order to evaluate the performance and risk of the storage system when processing a specific business model, it is necessary to input the current configuration of the system (such as processor core binding policy, memory usage, RAID configuration, etc.) and the target business model (such as concurrent access mode of the business, data access type, etc.) into the analysis model for further analysis and simulation.

[0118] Step S810: Obtain various adjustment configuration situations of the storage system fed back by the analysis model.

[0119] Specifically, the analysis model will evaluate and feedback various possible configuration adjustment plans, and each plan includes specific configuration combinations and parameters for testing and optimizing system performance. The analysis model evaluates the system performance and risks under each configuration combination by traversing and simulating different configuration combinations (such as adjusting processor core binding, changing RAID stripe width, optimizing memory allocation, etc.) and their parameters. Based on these adjustment configuration situations, further screening and decision-making are carried out.

[0120] Among them, each adjustment configuration situation includes the adjusted configuration combination and configuration parameters.

[0121] Step S820: Determine the risk indicators of the storage system under each adjustment configuration situation.

[0122] Specifically, in order to screen out the most suitable configuration adjustment plan, it is necessary to conduct a risk assessment on each plan to determine the risk indicators of the storage system under each adjustment configuration situation.

[0123] Step S830: Take an adjustment configuration situation whose corresponding risk indicator is less than or equal to the preset threshold as the target adjustment configuration situation.

[0124] Among them, the adjustment configuration situation includes the target configuration combination and target configuration parameters. From various adjustment configuration situations, screen out the configurations whose risk indicators are within the preset threshold as the target adjustment configuration situations to ensure that the system can operate safely and stably after adjustment. It can be to compare the risk indicators under different adjustment configuration situations and select the configuration with the lowest risk indicator and not exceeding the preset threshold as the target adjustment configuration situation.

[0125] In this embodiment, by quantifying the risk indicators under each adjustment configuration situation, system administrators or automated systems can scientifically compare and select, avoiding system instability or performance degradation caused by blind adjustment, and ensuring the security and efficient operation of the storage system. By selecting the target adjustment configuration situation, the system can strategically adjust the configuration, avoiding resource waste or performance mismatch caused by over-optimization, and ensuring that when the storage system processes the target business model, it can not only reduce risks but also maintain efficient and stable performance.

[0126] In one embodiment, as Figure 9 shown, Step S210: Determine the working performance parameters of the storage system when processing the target business model according to the current configuration situation. It includes: Steps S900 - S910:

[0127] Step S900: Input the current configuration of the storage system and the target business model into a preset prediction model.

[0128] Among them, the prediction model is obtained by training with a training sample set, and the training sample set includes historical performance parameters of the storage system under different configurations and when processing different business models.

[0129] Exemplarily, the prediction model can be an RNN model. Use the RNN model to analyze the input business model and configuration parameters, and according to the embedded theoretical performance database and RNN, predict the system performance under the current configuration conditions. The application of RNN is as follows. Under the input conditions based on the business model and configuration parameters, the calculation process of the long short-term memory (LSTM) network of RNN is simplified as follows (forget gate, candidate memory state, update memory cell state, input and output gates, etc. are not the focus of this application and are presented in a simplified manner):

[0130] Yt = f(Business Model, Processor Binding, Memory Config, RAID Stripe, Advanced Features, PerformanceDB)

[0131] Among them, Yt is the expected performance output value of the system under the current configuration (such as IOPS, bandwidth, etc.). Business Model is the input of the business model, including, for example, the number of concurrencies, access patterns, and more specifically, the permutations and combinations of large and small blocks / read and write / random order, which directly affect the performance prediction of the storage system. Processor Binding is the binding policy of the processor core, which affects the distribution of the system's computing power. Memory Config is the memory configuration that determines the response speed of the system in high-load or data-intensive tasks. RAID Stripe is the RAID stripe width that affects data distribution and read speed and is a key factor affecting storage performance. Advanced Features are advanced functions such as deduplication and compression, and whether they are enabled has a significant impact on the system load and performance. PerformanceDB is the theoretical performance data corresponding to the business model embedded in the module.

[0132] This simplified formula emphasizes the relationship between the input conditions and the system performance prediction, focusing on the specific performance optimization of the storage system. Through these steps, RNN outputs the performance prediction value of the system under the current conditions based on the input business model, configuration parameters, and theoretical performance numbers, ensuring that there is reliable reference data for traversing the fault scenarios.

[0133] Specifically, the current configuration data of the storage system (including processor core binding, memory configuration, RAID stripe width, etc.) and the target business model (describing the concurrent access pattern of the business, data access type, etc.) are fed into a preset prediction model as input.

[0134] Among them, the prediction model is trained based on historical data and can predict the performance parameters of the system according to the input configuration and business model. The prediction model is trained based on historical data and can learn and understand the impact of different configurations and business models on system performance. The training process of the performance prediction model (using RNN) involves collecting and using a large amount of historical data of the storage system, including performance parameters such as IOPS, bandwidth, and latency under different configurations and when processing different business models. Through training with these data, the model can learn the association between the configuration and business model and the performance parameters, and then predict the future performance. The model trained based on historical data can more accurately predict the performance of the system when processing a specific business model, improving the accuracy and reliability of the prediction, and providing strong data support for performance optimization and fault prevention.

[0135] Step S910: Determine the working performance parameters of the storage system when processing the target business model according to the output result of the prediction model.

[0136] Specifically, convert the output of the prediction model into specific performance parameters as the basis for performance evaluation and optimization. The prediction model outputs a series of predicted values of performance parameters based on the input current configuration and target business model, such as predicted IOPS, bandwidth, latency, etc. The closed-loop feedback correction module receives these predicted values and determines the expected working performance parameters of the storage system when processing the target business model by evaluating whether the metrics meet the business requirements.

[0137] In this embodiment, through the output of the prediction model, the performance of the storage system under a specific business model can be quantified, which helps the system administrator or the automated system to identify potential performance bottlenecks or optimization points in advance, ensure that the system can meet the business requirements, reduce the risk of performance mismatch or failure, and improve system stability and business continuity.

[0138] In one embodiment, as Figure 10 shown, the above method for adjusting the configuration of the storage system further includes steps S1000 - S1020:

[0139] Step S1000: Obtain the actual working performance parameters of the storage system when processing the target business model under the current configuration.

[0140] Specifically, in order to verify and evaluate the accuracy of the prediction model, it is necessary to collect the actual performance data of the storage system when processing the target business model under the current configuration.

[0141] Exemplarily, it can be periodically or when triggered by a specific event, to collect actual working performance parameters from the storage system, including IOPS, throughput, latency, CPU utilization, etc. These data reflect the actual operating status of the system under the target business model.

[0142] Step S1010, determine the error situation of the prediction model according to the actual working performance parameters and the predicted working performance parameters determined based on the output result of the prediction model.

[0143] Specifically, by comparing the actual working performance parameters with the predicted working performance parameters output by the prediction model, to quantify the error of the prediction model.

[0144] Exemplarily, compare the collected actual working performance parameters with the predicted working performance parameters output by the prediction model, and calculate the error value or error percentage. Statistical methods can be used to quantify the error, such as mean squared error (MSE), mean absolute error (MAE), or more complex error evaluation algorithms, to quantify the difference between the model prediction and the actual performance.

[0145] Step S1020, adjust the weight parameters of the prediction model according to the error situation.

[0146] Specifically, based on the error situation, adjust the prediction model to reduce the prediction error and improve the accuracy and reliability of the prediction.

[0147] Exemplarily, the weight parameters of the prediction model (such as RNN) are adjusted according to the error situation through the backpropagation algorithm. Identify the prediction parameters with higher errors, and update the weight parameters of the model through the backpropagation algorithm to minimize the prediction error. This process may require multiple iterations until the prediction error of the model reaches the preset acceptable range.

[0148] In this embodiment, by collecting the actual working performance parameters, it provides a real data basis for the subsequent evaluation of the accuracy of the prediction model, ensuring the scientificity and effectiveness of the model optimization process. The determination of the error situation provides a clear direction for the subsequent optimization of the model, helps to reduce the prediction deviation, and improves the application effect and reliability of the model in different business scenarios. By dynamically adjusting the weight parameters of the model, it can specifically improve the prediction ability of the model when dealing with a specific business model, enhance the generalization ability and adaptability of the model, ensure the accuracy and reliability of the storage system performance prediction, and play a key guiding role in performance optimization and fault prevention.

[0149] In one embodiment, as Figure 11 shown, step S200, obtain the current configuration situation of the storage system. Including: steps S1100 - S1120:

[0150] Step S1100, determine the target configuration metrics related to the performance parameters of the storage system.

[0151] Specifically, identify the key configuration items that affect the performance of the storage system, so as to subsequently focus on and optimize these configurations specifically.

[0152] Exemplarily, the acquisition module collects the performance parameters during the operation of the storage system, including the processor core binding policy, memory configuration, enabled status of advanced functions of the volume type, RAID stripe width, business model (such as the number of concurrencies, access mode, and more specific permutations and combinations of large / small blocks / read / write / random order), and current performance data (IOPS / bandwidth / latency / CPU utilization, etc.). Based on these parameters, the adaptive feedback management module uses analysis algorithms (such as multiple regression analysis, decision tree analysis, etc.) to determine which configuration metrics have a significant impact on the performance parameters. These configuration metrics may include the processor core binding policy, memory configuration, RAID stripe width, enabled status of advanced functions of the volume type, etc.

[0153] Step S1110, obtain the configuration parameters corresponding to the target configuration metrics of the storage system.

[0154] Specifically, after identifying the key configuration metrics that affect the performance, it is necessary to obtain the current configuration parameters of these metrics for subsequent comparison and adjustment.

[0155] Exemplarily, read the configuration parameters related to the target configuration metrics from the configuration database of the storage system. For example, if the processor core binding policy is determined as a key metric, the module will obtain the current processor core binding policy configuration; if the memory configuration is determined as a key metric, the current memory usage and allocation policy, etc. will be obtained.

[0156] Step S1120, obtain the current configuration status of the storage system according to the configuration parameters corresponding to the target configuration metrics.

[0157] Specifically, integrate the obtained target configuration metrics and their configuration parameters into a comprehensive description of the current configuration status of the storage system for subsequent analysis and comparison.

[0158] Exemplarily, integrate the configuration parameters corresponding to all target configuration metrics to form a data packet describing the current configuration status of the storage system. This data packet may include the processor core binding policy, memory configuration parameters, RAID stripe width settings, current status of advanced functions of the volume type, etc., providing a comprehensive view to evaluate the configuration of the storage system.

[0159] In this embodiment, by determining the target configuration metrics related to the performance parameters, it is possible to focus on the optimization of critical configurations, avoid blind adjustments to non-critical configurations, improve the optimization efficiency and effect, and at the same time reduce resource waste and interference with the normal operation of the system. Obtaining the configuration parameters corresponding to the target configuration metrics provides specific data support for subsequent performance analysis and configuration optimization, helps system administrators or automated systems to more accurately identify configuration problems and optimization directions, and ensures the scientificity and pertinence of performance optimization. By integrating the current configuration situation, the system can more comprehensively evaluate the configuration status of the storage system under a specific business model, provide a comprehensive perspective for performance analysis and optimization, and help identify potential problems and optimization potential in the configuration combination. This step also provides a basis for subsequent performance prediction and configuration adjustment, ensuring the coherence and systematicness of the performance optimization process.

[0160] In one embodiment, as Figure 12 shown, in step S230, when the risk metric is greater than the preset threshold, after adjusting the current configuration situation of the storage system so that the risk metric corresponding to the adjusted current configuration situation is less than or equal to the preset threshold, the configuration adjustment method of the storage system further includes: steps S1200 - S1210:

[0161] Step S1200, obtain the actual working performance parameters of the storage system in real time.

[0162] Specifically, the running state of the storage system can be monitored in real time to ensure that it can respond to performance fluctuations in a timely manner and prevent potential system failures or performance degradation.

[0163] Exemplarily, continuously monitor the operation of the storage system and collect the actual working performance parameters, including but not limited to key performance indicators such as IOPS (number of input / output operations per second), throughput, latency, CPU utilization, etc. These data are recorded regularly or when a triggering event (such as a performance drop) occurs through real-time stream processing technologies (such as Apache Kafka) or polling methods.

[0164] Step S1210, when the actual working performance parameters exceed the preset range, adjust the current configuration situation of the storage system so that the actual working performance parameters are within the preset range.

[0165] Specifically, when the monitored performance parameters exceed the preset safety or performance range, the system needs to take measures to adjust the configuration to restore the performance to the normal level and avoid service interruption or system failure.

[0166] Exemplarily, the actual working performance parameters are obtained and compared with the preset performance range. If it is found that the performance parameters exceed the preset range, the configuration adjustment process will be initiated. The method in the above embodiment is used to automatically adjust the processor core binding policy, optimize the memory allocation, adjust the RAID configuration, or manage the enabling and disabling of advanced functions. The adjustment process follows the configuration action grading list to ensure that the adjustment actions are carried out within the range with the least impact on the business, avoiding additional interference with system stability.

[0167] In this embodiment, obtaining the actual working performance parameters in real time can not only ensure that the system administrator or the automation system has a comprehensive understanding of the running state of the storage system, but also can timely detect signs of performance degradation, provide timely data support for subsequent performance adjustment and optimization, improve the system's response speed and adaptive ability, reduce the operation and maintenance costs, enhance the business continuity and user experience. By dynamically adjusting the configuration of the storage system, it can respond to performance fluctuations in a timely manner, restore the performance parameters to the preset normal range, and ensure the stable operation and efficient performance of the system under various business models. This method reduces the impact of performance degradation or failures on the business and improves the reliability of the system and user satisfaction.

[0168] Finally, an example of the application scenario of the method of the present application is given.

[0169] Application scenario 1: Dynamic performance optimization during peak business periods:

[0170] During high business pressure periods, the storage system in the data center needs to handle a significantly increased concurrent load. The system predicts performance by inputting the business model (including the number of concurrent requests and access frequency) and the existing configuration parameters (processor core binding, memory configuration, RAID stripe width, etc.). The RNN model predicts the response latency and IOPS that the current configuration may generate based on these input conditions.

[0171] 1. Performance prediction: The prediction result obtained by the RNN model shows that the current configuration may experience a situation where the latency is close to the upper limit during the load peak period.

[0172] 2. Configuration adjustment: After the fault and stress scenario traversal analysis module (based on DQN) simulates the configuration adjustment, it is recommended to adjust the processor core binding relationship and optimize the memory access configuration, and suspend non-critical background deduplication and compression operations to release more resources for the foreground business.

[0173] 3. Feedback adjustment: The adaptive feedback management module implements the adjustment and monitors the system performance in real time to ensure that the latency is maintained within an acceptable range and the system runs stably.

[0174] Application scenario 2: Fault warning and improvement of fault tolerance ability:

[0175] During daily operations, there is a risk of failure in one of the controllers of the storage system. The failure of this controller may pose a threat to the continuity of the overall business. The system first conducts performance prediction during normal operation through the business model and configuration conditions, and inputs the fault scenario into the deep reinforcement learning module to simulate the impact after the controller fails.

[0176] 1. Fault scenario simulation: The deep Q-learning module simulates the system performance after the controller fails, analyzes the possible extent of performance degradation, and finds that there is a high risk of a significant reduction in the system's IOPS.

[0177] 2. Optimization configuration: The system feeds back and automatically reallocates processor cores, switches the load from the faulty controller to other normally operating controllers, and at the same time adjusts the RAID configuration to improve the read and write speed.

[0178] 3. Performance verification and feedback: The feedback management module is used to confirm that the new configuration can meet the business requirements in case of a fault, and finally improve the fault tolerance of the system under controller failure.

[0179] Application scenario three: Resource conservation under a dynamic business model:

[0180] The storage system of a financial enterprise needs to process batch data analysis tasks at night, while mainly handling user access and transaction requests during the day. This business model requires the system to dynamically adjust performance according to the time period to save resources. The system inputs the different requirements for day and night according to the business model, and conducts performance prediction and optimization configuration respectively.

[0181] 1. Business model prediction: When the concurrent requests are high during the day, the RNN prediction model outputs results with higher response time and IOPS requirements, while in the batch tasks at night, the system focuses on data throughput.

[0182] 2. Automatic configuration switching: The adaptive feedback management module adjusts the configuration according to the day and night requirements predicted by the RNN respectively. For example, data compression and deduplication functions are enabled at night to improve data storage efficiency.

[0183] 3. Performance monitoring and feedback: The system verifies the configuration effect during business switching, and dynamically adjusts the performance prediction model through the feedback management module to achieve a dynamic balance between resource conservation and business requirements.

[0184] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of the various embodiments of the present application.

[0185] In this embodiment, an adjustment device for the configuration of a storage system is further provided to implement the above embodiments and preferred implementation manners. Those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that can implement a predetermined function. Although the modules described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0186] Figure 13 is a structural block diagram of an adjustment device for the configuration of a storage system according to an embodiment of the present application. The device includes:

[0187] A configuration situation acquisition module 1301, configured to acquire the current configuration situation of the storage system, where the configuration situation includes a configuration combination and configuration parameters.

[0188] A working performance parameter determination module 1302, configured to determine the working performance parameters of the storage system when processing a target service model according to the current configuration situation.

[0189] A risk index determination module 1303, configured to determine the risk index of the storage system when in the working performance parameters according to the current configuration situation and the working performance parameters of the storage system.

[0190] An adjustment module 1304, configured to adjust the current configuration situation of the storage system when the risk index is greater than a preset threshold, so that the risk index corresponding to the adjusted current configuration situation is less than or equal to the preset threshold.

[0191] The above device predicts the working performance parameters when processing the target business model by obtaining the current configuration of the storage system, and analyzes the risk indicators in combination with the fault scenario and the load scenario. The evaluation of the risk indicators can help the system identify potential problems and performance bottlenecks, provide a quantitative basis for subsequent configuration adjustment, and ensure that the system can respond in a timely manner and maintain stable performance output under complex business and fault scenarios. Thus, it is possible to evaluate the rationality and security of the configuration before the system runs, avoid performance bottlenecks or fault risks caused by improper configuration, and improve the stability and efficiency of the storage system. In summary, through the closed-loop feedback mechanism and configuration adjustment, the system can dynamically optimize its own configuration to cope with changing business models and potential fault risks. In this way, even under high-load or fault scenarios, the storage system can maintain stable performance and high availability, improving the reliability and business continuity of the storage system.

[0192] In an exemplary embodiment, the risk indicator determination module 1303 further includes:

[0193] A test unit for respectively simulating the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios, where the configuration of the simulated storage system is the current configuration, and the performance parameters of the simulated storage system are the working performance parameters.

[0194] A risk indicator determination unit for determining risk indicators according to the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios.

[0195] In an exemplary embodiment, the test unit is further configured to determine the potential fault conditions of the storage system according to the configuration types of the storage system. Construct a variety of fault scenarios according to the potential fault conditions, where each fault scenario corresponds to a potential fault condition. Input the storage system and a variety of fault scenarios into a preset analysis model to respectively simulate the performance of the storage system in a variety of fault scenarios.

[0196] In an exemplary embodiment, the test unit is further configured to determine the potential load conditions of the storage system according to a variety of business models corresponding to the storage system. Construct a variety of load scenarios according to the potential load conditions, where each load scenario corresponds to a potential load condition. Input the storage system and a variety of load scenarios into a preset analysis model to respectively simulate the performance of the storage system in a variety of load scenarios.

[0197] In an exemplary embodiment, the risk indicator determination unit is further configured to determine a fault risk indicator according to the performance of the storage system in a variety of different fault scenarios. Determine a pressure risk indicator according to the performance of the storage system in a variety of different load scenarios. Determine the risk indicator according to the fault risk indicator and the pressure risk indicator.

[0198] In an exemplary embodiment, the adjustment module 1304 includes:

[0199] A target configuration determination unit, configured to, when the risk indicator is greater than a preset threshold, input the current configuration of the storage system and the target service model into an analysis model, so as to determine a target configuration combination and target configuration parameters that enable the risk indicator of the storage system to be less than or equal to the preset threshold when processing the target service model.

[0200] An adjustment unit, configured to adjust the current configuration of the storage system according to the target configuration combination and the target configuration parameters.

[0201] In an exemplary embodiment, the above device further includes: The target configuration determination unit is further configured to input the current configuration of the storage system and the target service model into the analysis model. Obtain various adjustment configuration situations of the storage system fed back by the analysis model, where each adjustment configuration situation includes an adjusted configuration combination and configuration parameters. Determine the risk indicator of the storage system in each adjustment configuration situation. Use an adjustment configuration situation in which the corresponding risk indicator is less than or equal to the preset threshold as the target adjustment configuration situation, where the adjustment configuration situation includes the target configuration combination and the target configuration parameters.

[0202] In an exemplary embodiment, the working performance parameter determination module 1302 is further configured to input the current configuration of the storage system and the target service model into a preset prediction model, where the prediction model is trained using a training sample set, and the training sample set includes historical performance parameters of the storage system when processing different service models under different configurations. Determine the working performance parameters of the storage system when processing the target service model according to the output result of the prediction model.

[0203] In an exemplary embodiment, the above device further includes:

[0204] An actual parameter acquisition module, configured to acquire the actual working performance parameters of the storage system when processing the target service model in the current configuration.

[0205] An error determination module, configured to determine the error situation of the prediction model according to the actual working performance parameters and the predicted working performance parameters determined based on the output result of the prediction model.

[0206] A weight adjustment module, configured to adjust the weight parameters of the prediction model according to the error situation.

[0207] In an exemplary embodiment, the configuration situation acquisition module 1301 includes:

[0208] A target index determination unit, configured to determine a target configuration index related to the performance parameters of the storage system.

[0209] A configuration parameter acquisition unit, configured to acquire configuration parameters corresponding to target configuration metrics of a storage system.

[0210] A current configuration situation determination unit, configured to obtain the current configuration situation of the storage system according to the configuration parameters corresponding to the target configuration metrics.

[0211] In an exemplary embodiment, the above device further includes:

[0212] A real-time parameter acquisition module, configured to acquire actual working performance parameters of the storage system in real time.

[0213] A feedback adjustment module, configured to adjust the current configuration situation of the storage system when the actual working performance parameters exceed a preset range, so that the actual working performance parameters are within the preset range.

[0214] It should be noted that the above-mentioned modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to this: the above-mentioned modules are all located in the same processor. Or, the above-mentioned modules are respectively located in different processors in any combination form.

[0215] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, and the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0216] Optionally, in this embodiment, the above computer program can be configured to execute the following steps by a computer program:

[0217] S1. Obtain the current configuration situation of the storage system, where the configuration situation includes a configuration combination and configuration parameters.

[0218] S2. Determine the working performance parameters of the storage system when processing the target service model according to the current configuration situation.

[0219] S3. Determine the risk metrics of the storage system when in the working performance parameters according to the current configuration situation of the storage system and the working performance parameters.

[0220] S4. When the risk metric is greater than a preset threshold, adjust the current configuration situation of the storage system so that the risk metric corresponding to the adjusted current configuration situation is less than or equal to the preset threshold.

[0221] In an exemplary embodiment, the above computer-readable storage medium may include, but is not limited to: various media that can store computer programs such as USB flash drives, read-only memory (ROM for short), random access memory (RAM for short), mobile hard disks, magnetic disks, or optical discs.

[0222] An embodiment of the present application also provides an electronic device, as Figure 14 shown, the electronic device includes a memory 1402 and a processor 1404. A computer program is stored in the memory 1402, and the processor 1404 is configured to execute the steps in any of the above method embodiments through the computer program.

[0223] Optionally, in this embodiment, the above processor 1404 may be configured to execute the following steps through the computer program:

[0224] S1, obtain the current configuration of the storage system, where the configuration includes a configuration combination and configuration parameters.

[0225] S2, determine the working performance parameters of the storage system when processing the target business model according to the current configuration.

[0226] S3, determine the risk index of the storage system when in the working performance parameters according to the current configuration of the storage system and the working performance parameters.

[0227] S4, in the case where the risk index is greater than the preset threshold, adjust the current configuration of the storage system so that the risk index corresponding to the adjusted current configuration is less than or equal to the preset threshold.

[0228] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details are not described herein again.

[0229] Optionally, those of ordinary skill in the art can understand that Figure 14 the structure shown is only schematic Figure 14 and does not limit the structure of the above electronic device. For example, the electronic device may further include more or fewer components (such as a network interface, etc.) than Figure 14 shown, or have a different configuration from Figure 14 shown.

[0230] Among them, the memory 1402 can be used to store software programs and modules, such as the program instructions / modules corresponding to the adjustment method of the storage system configuration and the adjustment device of the storage system configuration in the embodiments of the present application. The processor 1404 executes various functional applications and data processing by running the software programs and modules stored in the memory 1402, that is, implements the above-mentioned adjustment method of the storage system configuration. The memory 1402 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 1402 may further include a memory remotely set relative to the processor 1404, and these remote memories can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and combinations thereof. Among them, the memory 1402 can specifically but not limitedly be used to store information such as system configuration files. As an example, as Figure 14 shown, the above memory 1402 may include but is not limited to the modules in the above adjustment device of the storage system configuration. In addition, it may also include but is not limited to other module units in the above adjustment device of the storage system configuration, which will not be elaborated in this example.

[0231] Optionally, the above transmission device 1406 is used to receive or send data via a network. Specific examples of the above network may include a wired network and a wireless network. In one instance, the transmission device 1406 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers through a network cable, thereby enabling communication with the Internet or a local area network. In one instance, the transmission device 1406 is a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0232] In addition, the above electronic device further includes: a display 1408; and a connection bus 1410, which is used to connect each module component in the above electronic device.

[0233] In other embodiments, the above electronic device may be a node in a distributed system. Among them, the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting multiple nodes through network communication. Among them, the nodes can form a peer-to-peer (P2P, Peer To Peer) network, and any form of computing device, such as a server, a terminal, and other electronic devices, can become a node in the blockchain system by joining the peer-to-peer network.

[0234] Embodiments of the present application also provide a computer program product. The computer program product includes a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0235] Embodiments of the present application also provide another computer program product, including a non-volatile computer-readable storage medium. The non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0236] Embodiments of the present application also provide a computer program. The computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the steps in any of the above method embodiments.

[0237] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general-purpose computing device. They can be concentrated on a single computing device or distributed on a network composed of multiple computing devices. They can be implemented by program codes executable by the computing device. Thus, they can be stored in a storage device and executed by the computing device. And in some cases, the steps shown or described can be executed in a different order than here, or they can be separately fabricated into individual integrated circuit modules, or multiple modules or steps among them can be fabricated into a single integrated circuit module to be implemented. In this way, the present application is not limited to any specific combination of hardware and software.

[0238] The above are only the preferred embodiments of the present application and are not used to limit the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for adjusting the configuration of a storage system, characterized in that The method includes: Obtaining the current configuration of the storage system, where the configuration includes a configuration combination and configuration parameters; Determining the working performance parameters of the storage system when processing a target business model according to the current configuration; Respectively simulating the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios, where the configuration of the simulated storage system is the current configuration, and the performance parameters of the simulated storage system are the working performance parameters. The variety of different fault scenarios are determined according to the potential fault conditions of the storage system, and the variety of different load scenarios are determined according to the potential load conditions of the storage system; Determining a risk indicator according to the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios; When the risk indicator is greater than a preset threshold, inputting the current configuration of the storage system and the target business model into a preset analysis model to determine a target configuration combination and target configuration parameters that can make the risk indicator less than or equal to the preset threshold when the storage system processes the target business model. The analysis model is used to simulate and traverse different configuration combinations to find the configuration combination and configuration parameters that can reduce the risk indicator below the preset threshold; Updating the current configuration of the storage system according to the target configuration combination and the target configuration parameters, so that the risk indicator corresponding to the adjusted current configuration is less than or equal to the preset threshold. When adjusting the current configuration of the storage system, query a preset configuration action grading list. In the configuration action grading list, multiple configuration items are graded according to the impact degree of the configuration on the business, and the actions of high-level configurations require authorization.

2. The method for adjusting the configuration of the storage system according to claim 1, characterized in that The step of respectively simulating the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios includes: Determining the potential fault conditions of the storage system according to the configuration types of the storage system; Constructing a variety of fault scenarios according to the potential fault conditions, where each fault scenario corresponds to a potential fault condition; Inputting the storage system and the variety of fault scenarios into a preset analysis model to respectively simulate the performance of the storage system in the variety of fault scenarios.

3. The method for adjusting the configuration of the storage system according to claim 2, wherein, The step of respectively simulating the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios further includes: Determining the potential load conditions of the storage system according to a variety of business models corresponding to the storage system; Constructing a variety of load scenarios according to the potential load conditions, where each load scenario corresponds to a potential load condition; Inputting the storage system and the variety of load scenarios into the preset analysis model to respectively simulate the performance of the storage system in the variety of load scenarios.

4. The method for adjusting the configuration of the storage system according to claim 3, wherein The step of determining the risk indicator according to the performance of the storage system in a variety of different fault scenarios and a variety of different load scenarios includes: Determining a fault risk indicator according to the performance of the storage system in a variety of different fault scenarios; Determine a pressure risk indicator according to the performance of the storage system in a variety of different load scenarios; Determine the risk indicator according to the failure risk indicator and the pressure risk indicator.

5. The method for adjusting the configuration of the storage system according to claim 4, characterized in that, When the risk indicator is greater than the preset threshold, input the current configuration of the storage system and the target service model into the analysis model to determine a target configuration combination and target configuration parameters that make the risk indicator less than or equal to the preset threshold when the storage system processes the target service model, including: Input the current configuration of the storage system and the target service model into the analysis model; Obtain various adjusted configuration situations of the storage system fed back by the analysis model, where each adjusted configuration situation includes an adjusted configuration combination and configuration parameters; Determine the risk indicator of the storage system in each adjusted configuration situation; Take an adjusted configuration situation with a corresponding risk indicator less than or equal to the preset threshold as the target adjusted configuration situation, where the adjusted configuration situation includes the target configuration combination and target configuration parameters.

6. The method for adjusting the configuration of the storage system according to any one of claims 1-5, characterized in that Determine the working performance parameters of the storage system when processing the target service model according to the current configuration situation, including: Input the current configuration of the storage system and the target service model into a preset prediction model, where the prediction model is trained using a training sample set, and the training sample set includes historical performance parameters of the storage system when processing different service models under different configurations; Determine the working performance parameters of the storage system when processing the target service model according to the output result of the prediction model.

7. The method for adjusting the configuration of the storage system according to claim 6, wherein The method further includes: Obtain the actual working performance parameters of the storage system when processing the target service model in the current configuration; Determine the error situation of the prediction model according to the actual working performance parameters and the predicted working performance parameters determined based on the output result of the prediction model; Adjust the weight parameters of the prediction model according to the error situation.

8. The method for adjusting the configuration of the storage system according to claim 6, characterized in that, Obtaining the current configuration of the storage system includes: Determine the target configuration indicators related to the performance parameters of the storage system; Obtain the configuration parameters corresponding to the target configuration indicators of the storage system; Obtain the current configuration of the storage system according to the configuration parameters corresponding to the target configuration indicators.

9. The method for adjusting the configuration of the storage system according to any one of claims 1-5, characterized in that After adjusting the current configuration of the storage system when the risk indicator is greater than the preset threshold so that the risk indicator corresponding to the adjusted current configuration is less than or equal to the preset threshold, the method further includes: Real-time obtain the actual working performance parameters of the storage system; When the actual working performance parameters exceed the preset range, adjust the current configuration of the storage system so that the actual working performance parameters are within the preset range.

10. An adjustment device for the configuration of a storage system, characterized in that The device includes: A configuration situation acquisition module, configured to acquire the current configuration of the storage system, where the configuration situation includes a configuration combination and configuration parameters; A working performance parameter determination module, configured to determine, according to the current configuration situation, the working performance parameters of the storage system when processing the target service model; A risk index determination module, configured to respectively simulate the performance of the storage system in a plurality of different fault scenarios and a plurality of different load scenarios. The configuration situation of the simulated storage system is the current configuration situation, and the performance parameters of the simulated storage system are the working performance parameters. The plurality of different fault scenarios are determined according to the potential fault situation of the storage system, and the plurality of different load scenarios are determined according to the potential load situation of the storage system; determine the risk index according to the performance of the storage system in a plurality of different fault scenarios and a plurality of different load scenarios; An adjustment module, configured to, when the risk index is greater than a preset threshold, input the current configuration situation of the storage system and the target service model into a preset analysis model to determine a target configuration combination and target configuration parameters that can make the risk index less than or equal to the preset threshold when the storage system processes the target service model. The analysis model is used to simulate and traverse different configuration combinations to find a configuration combination and configuration parameters that can reduce the risk index below the preset threshold; update the current configuration situation of the storage system according to the target configuration combination and the target configuration parameters, so that the risk index corresponding to the adjusted current configuration situation is less than or equal to the preset threshold. When adjusting the current configuration situation of the storage system, query a preset configuration action grading list, and the configuration action grading list grades a plurality of configuration items according to the influence degree of the configuration on the service. Actions of configurations with a high level require authorization.

11. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium, wherein when the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 9 are implemented.

12. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method described in any one of claims 1 to 9 are implemented.

13. A computer program product, comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method described in any one of claims 1 to 9.

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