Equipment performance optimization method, device, equipment, medium and program product

Abstract

The present application provides a device performance optimization method, apparatus, device, medium, and program product, which can be applied in the field of computer technology. The method includes: determining a comprehensive performance evaluation value for each of the multiple target devices based on multiple robustness indicators for multiple target devices in a server cluster; determining, for any target device among the multiple target devices, a performance reallocation value for the target device based on the performance deviation value of the target device and the comprehensive performance evaluation value of the target device when the performance deviation value of the target device is outside a preset threshold range; determining, based on a parameter mapping relationship, a performance parameter adjustment value for the target device based on a performance adjustment ratio for the target device determined using the performance reallocation value and the comprehensive performance evaluation value of the target device; and updating configuration parameters of the target device using the performance parameter adjustment value to optimize the performance of the target device.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and more specifically, to a device performance optimization method, apparatus, device, medium, and program product. Background Art

[0002] With the rapid development of high-performance computing, artificial intelligence, and big data applications, the high-speed Peripheral Component Interconnect express (PCIe) bus, as the most important high-speed serial computer expansion bus in server clusters, ensures the robustness and reliability of the performance of devices in the PCIe link (hereinafter referred to as PCIe devices) to ensure the normal and stable operation of the entire PCIe link.

[0003] In related technologies, PCIe device performance management primarily relies on static configuration or manual intervention. Specifically, when PCIe device performance imbalances occur, manual troubleshooting and configuration adjustments are often required. This is not only inefficient but can also lead to service interruptions in critical business scenarios. Summary of the Invention

[0004] In view of this, the present application provides a device performance optimization method, apparatus, device, medium and program product.

[0005] According to one aspect of the present application, a device performance optimization method is provided, comprising: determining, based on multiple robustness indicators for multiple target devices in a server cluster, a comprehensive performance evaluation value of each of the multiple target devices, wherein the target devices are devices in the server cluster that communicate with a processor based on a high-speed serial computer expansion bus; for any target device among the multiple target devices, when a performance deviation value of the any target device is outside a preset threshold range, determining a performance reallocation value of the any target device based on the performance deviation value of the any target device and the comprehensive performance evaluation value of the any target device, wherein the performance deviation value represents a degree of deviation between the comprehensive performance of the any target device and the average comprehensive performance of the multiple target devices; based on a parameter mapping relationship, determining a performance parameter adjustment value of the any target device according to a performance adjustment ratio of the any target device determined using the performance reallocation value and the comprehensive performance evaluation value of the any target device, and updating configuration parameters of the any target device using the performance parameter adjustment value, so as to optimize the performance of the any target device.

[0006] According to another aspect of the present application, a device performance optimization apparatus is provided, comprising: a first determination module for determining, based on a plurality of robustness indicators for a plurality of target devices in a server cluster, a comprehensive performance evaluation value of each of the plurality of target devices, wherein the target devices are devices in the server cluster that communicate with a processor based on a high-speed serial computer expansion bus; a second determination module for determining, for any target device among the plurality of target devices, a performance reallocation value of the any target device based on the performance deviation value of the any target device and the comprehensive performance evaluation value of the any target device when the performance deviation value of the any target device is outside a preset threshold range, wherein the performance deviation value represents a degree of deviation between the comprehensive performance of the any target device and the average comprehensive performance of the plurality of target devices; a parameter updating module for determining, based on a parameter mapping relationship, a performance parameter adjustment value of the any target device according to a performance adjustment ratio of the any target device determined using the performance reallocation value and the comprehensive performance evaluation value of the any target device, and updating configuration parameters of the any target device using the performance parameter adjustment value to optimize the performance of the any target device.

[0007] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0008] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the steps of the above method are implemented.

[0009] According to another aspect of the present application, a computer program product is provided, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0010] According to an embodiment of the present application, by determining the comprehensive performance evaluation values of the multiple target devices according to the multiple robustness indicators of the multiple target devices in the server cluster, it is possible to automatically obtain an evaluation value reflecting the comprehensive performance of the multiple target devices. By determining the performance reallocation value of any target device according to the performance deviation value of any target device and the comprehensive performance evaluation value of any target device when the performance deviation value of any target device is outside the preset threshold range, it is possible to automatically calculate the performance reallocation value of any target device when the performance deviation value of any target device is outside the preset threshold range, that is, when the performance of any target device is unbalanced, to achieve dynamic performance allocation. At the same time, based on the parameter mapping relationship, according to the performance adjustment ratio of any target device determined by using the performance reallocation value and the comprehensive performance evaluation value of any target device, the performance parameter adjustment value of any target device is determined, and the configuration parameters of any target device are updated using the performance parameter adjustment value to optimize the performance of any target device, so as to achieve automatic repair of the performance of any target device through a dynamic repair strategy, so that the performance of any target device remains balanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other objects, features and advantages of the present application will become more apparent through the following description of the embodiments of the present application with reference to the accompanying drawings.

[0012] Figure 1 An exemplary system architecture to which the device performance optimization method according to an embodiment of the present application can be applied is shown.

[0013] Figure 2 A flow chart of a device performance optimization method according to an embodiment of the present application is shown.

[0014] Figure 3 A flow chart of a device performance optimization method according to another embodiment of the present application is shown.

[0015] Figure 4 A block diagram of a device performance optimization apparatus according to an embodiment of the present application is shown.

[0016] Figure 5 A block diagram of an electronic device suitable for implementing the device performance optimization method described above according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0017] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present application. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present application.

[0018] The terms used herein are only for describing specific embodiments and are not intended to limit this application. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0019] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0020] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0021] In real-world applications, due to hardware design flaws, topology defects, uneven loads, or environmental factors, PCIe devices can experience uneven performance distribution, leading to overall performance degradation in the service cluster. Even in scenarios where the service cluster is a storage cluster containing multiple nodes, uneven PCIe device performance can lead to data synchronization anomalies, ultimately causing storage system failure or crash.

[0022] In related technologies, PCIe device performance management primarily relies on static configuration or manual intervention, lacking dynamic adjustment capabilities. This results in low adjustment efficiency and can cause service interruptions in critical business scenarios. Furthermore, traditional dynamic performance adjustment methods often employ simple speed reduction strategies, such as device restarts, lack intelligent decision-making mechanisms, and are unable to dynamically adapt to complex and changing load scenarios, making it difficult to optimize performance while ensuring server cluster stability.

[0023] In view of this, the present application provides a device performance optimization method that can be applied in the field of computer technology.

[0024] An embodiment of the present application provides a device performance optimization method, comprising determining a comprehensive performance evaluation value of each of the multiple target devices based on multiple robustness indicators for the multiple target devices in a server cluster, wherein the target device is a device in the server cluster that communicates with a processor based on a high-speed serial computer expansion bus; for any target device among the multiple target devices, when a performance deviation value of any target device is outside a preset threshold range, determining a performance reallocation value of any target device based on the performance deviation value of any target device and the comprehensive performance evaluation value of any target device, wherein the performance deviation value represents the degree of deviation between the comprehensive performance of any target device and the average comprehensive performance of the multiple target devices; based on a parameter mapping relationship, determining a performance parameter adjustment value of any target device according to a performance adjustment ratio of any target device determined using the performance reallocation value and the comprehensive performance evaluation value of any target device, and updating the configuration parameters of any target device using the performance parameter adjustment value to optimize the performance of any target device.

[0025] Figure 1 The following shows an exemplary system architecture to which the device performance optimization method according to an embodiment of the present application can be applied. Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present application can be applied, to help those skilled in the art understand the technical content of the present application, but does not mean that the embodiments of the present application cannot be used in other devices, systems, environments or scenarios.

[0026] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a server 101, a communication link 102, a first target device D1, a second target device D2, ..., an Mth target device D1 in a server cluster, and a communication link 102. M , where M is an integer greater than 2. The first target device D1, the second target device D2, ..., the Mth target device D M A device that communicates based on the PCIe bus.

[0027] Server 101, first target device D1, second target device D2, ..., Mth target device D M Communication can be performed between them via a communication link 102. The communication link 102 can be a PCIe link.

[0028] The server 101 can automatically detect the first target device D1, the second target device D2, ..., the Mth target device D1 in real time. M And according to the device performance optimization method provided by this application, the first target device D1, the second target device D2, ..., the Mth target device D MThe performance indicator data is processed to automatically optimize the performance of any target device. The server 101 can be a server at the master node of the server cluster.

[0029] It should be noted that the device performance optimization method provided in the embodiment of the present application can generally be executed by the server 101. Accordingly, the device performance optimization apparatus provided in the embodiment of the present application can generally be set in the server 101. The device performance optimization method provided in the embodiment of the present application can also be performed by a server different from the server 101 and capable of communicating with the first target device D1, the second target device D2, ..., the Mth target device D M Accordingly, the device performance optimization apparatus provided in the embodiment of the present application can also be set up on a server different from the server 101 and can communicate with the first target device D1, the second target device D2, ..., the Mth target device D M Communicating servers or server clusters.

[0030] It should be understood that Figure 1 The number of target devices, communication links and servers in the embodiment is only for illustration. Any number of target devices, communication links and servers may be provided as required.

[0031] Figure 2 A flow chart of a device performance optimization method according to an embodiment of the present application is shown.

[0032] like Figure 2 As shown, the device performance optimization method includes operations S201 to S203.

[0033] In operation S201 , comprehensive performance evaluation values of respective target devices in a server cluster are determined based on a plurality of robustness indicators for the target devices.

[0034] The target device is a device in a server cluster that communicates with the processor based on a high-speed serial computer expansion bus.

[0035] For example, the target device can be any device in a PCIe link in a server cluster. The target devices communicate over a high-speed serial computer expansion bus. The server cluster can be used for high-speed computing or large data storage. The server cluster can be a storage cluster or a computing cluster.

[0036] For example, server 101 can deploy an encrypted container using the Organic SAS Enclosure Service (OSES). The specific deployment steps include preparing a container (Docker) environment; preparing a data acquisition format, writing a container file (Dockerfile) containing encryption instructions to build a container image and encrypt the container; and running the encrypted container. Because encrypted containers offer numerous advantages, such as being lightweight, easy to deploy, highly real-time, efficient in resource utilization, and capable of storing large amounts of data, they can be used to conveniently acquire a large amount of metric data corresponding to multiple robustness indicators for multiple target devices in the server cluster. Furthermore, encrypting the container ensures data security and reliability.

[0037] For example, a Proportional-Integral-Differential (PID) control algorithm may be used to process multiple robustness indicators for multiple target devices in a server cluster to determine comprehensive performance evaluation values of each of the multiple target devices.

[0038] In operation S202, for any target device among the multiple target devices, if a performance deviation value of the target device is outside a preset threshold range, a performance reallocation value for the target device is determined based on the performance deviation value of the target device and a comprehensive performance evaluation value of the target device. The performance deviation value represents the degree of deviation between the comprehensive performance of the target device and the average comprehensive performance of the multiple target devices.

[0039] According to the embodiments of the present application, if the performance deviation value of any target device falls outside a preset threshold range, the target device's performance is unbalanced, but the overall performance of the server cluster is not affected. In this case, dynamic optimization of the target device's performance can timely adjust and balance the performance of the target device, avoiding further imbalance in the target device's performance and the impact on the overall performance of the server cluster.

[0040] According to an embodiment of the present application, the preset threshold range can be selected according to actual conditions and is not limited here. For example, the preset threshold range can be [-0.03, 0.03]. Alternatively, the preset threshold range can be [-0.04, 0.04]. Alternatively, the preset threshold range can be [-0.05, 0.05].

[0041] For example, the performance compensation value of any target device can be calculated based on the performance deviation value of any target device and the total performance value of multiple target devices. The performance compensation value of any target device and the comprehensive performance evaluation value of any target device are added together to obtain the performance redistribution value of any target device.

[0042] In operation S203, based on the parameter mapping relationship, the performance parameter adjustment value of any target device is determined according to the performance adjustment ratio of the target device determined by using the performance reallocation value and the comprehensive performance evaluation value of any target device, and the configuration parameters of any target device are updated using the performance parameter adjustment value.

[0043] According to an embodiment of the present application, the performance of any target device is optimized by updating the configuration parameters of any target device using the performance parameter adjustment value.

[0044] According to an embodiment of the present application, by determining the comprehensive performance evaluation values of the multiple target devices according to the multiple robustness indicators of the multiple target devices in the server cluster, it is possible to automatically obtain an evaluation value reflecting the comprehensive performance of the multiple target devices. By determining the performance reallocation value of any target device according to the performance deviation value of any target device and the comprehensive performance evaluation value of any target device when the performance deviation value of any target device is outside the preset threshold range, it is possible to automatically calculate the performance reallocation value of any target device when the performance deviation value of any target device is outside the preset threshold range, that is, when the performance of any target device is unbalanced, to achieve dynamic performance allocation. At the same time, based on the parameter mapping relationship, according to the performance adjustment ratio of any target device determined by using the performance reallocation value and the comprehensive performance evaluation value of any target device, the performance parameter adjustment value of any target device is determined, and the configuration parameters of any target device are updated using the performance parameter adjustment value to optimize the performance of any target device, so as to achieve automatic repair of the performance of any target device through a dynamic repair strategy, so that the performance of any target device remains balanced.

[0045] According to the device performance optimization method provided in this application, the performance of any target device can be automatically repaired through a dynamic repair strategy, and then it can dynamically adapt to complex and changing load scenarios, optimizing performance while ensuring the stability of the server cluster.

[0046] According to an embodiment of the present application, the multiple robustness indicators include at least two of bandwidth, input / output operations per second (IOPS), latency, and error rate.

[0047] According to embodiments of the present application, bandwidth reflects the total amount of data that a target device can transmit per second. Read / write operations per second reflects the number of input / output operations that a target device can handle per second. Latency reflects the time it takes for data corresponding to a target device to be transmitted from a sender to a receiver. Error rate reflects the error rate of data received or sent by the target device.

[0048] For example, read bandwidth, write bandwidth, and bidirectional bandwidth can be used as sub-performance indicators under the bandwidth metric to calculate the bandwidth metric's value. Read IOPS, write IOPS, and bidirectional IOPS can be used as sub-performance indicators under the read and write operations per second metric to calculate the read and write operations per second metric's value. One-way latency, round-trip latency, and memory read and write latency can be used as sub-performance indicators under the latency metric to calculate the latency metric's value. Bit error rate, transmission error rate, and link error rate can be used as sub-performance indicators under the error rate metric to calculate the error rate metric's value.

[0049] For example, multiple robustness metrics include bandwidth, read and write operations per second, latency, and error rate.

[0050] According to the embodiments of the present application, the bandwidth, number of read and write operations per second, latency and error rate can overall reflect the efficiency, speed and error rate of the target device in processing data, and then the comprehensive performance of the target device can be evaluated more objectively and accurately based on the bandwidth, number of read and write operations per second, latency and error rate.

[0051] For example, the coefficient of variation method may be used to process multiple robustness indicators for multiple target devices in a server cluster to determine comprehensive performance evaluation values of each of the multiple target devices.

[0052] According to the embodiments of the present application, the core idea of the coefficient of variation method is to use the coefficient of variation obtained based on the statistical information of each performance indicator to reflect the data volatility. The more volatile the indicator is, the more significant the impact on the overall performance of the target device is, and the higher the weight is given. It is suitable for scenarios in which the indicators in the performance scenario have different dimensions and require objective evaluation.

[0053] For example, for Figure 2 Operation S201 shown, determining the comprehensive performance evaluation value of each of the multiple target devices based on the multiple robustness indicators for the multiple target devices in the server cluster, may include the following operations: normalizing the multiple sub-performance indicators corresponding to the robustness indicators of the target devices to obtain multiple normalized sub-indicators; normalizing the statistical information of the multiple normalized sub-indicators corresponding to the multiple robustness indicators to determine multiple first weights; and weighting the multiple robustness indicators using the multiple first weights to determine the comprehensive performance evaluation value of the target device.

[0054] For example, the robustness index can be obtained by performing a weighted summation on multiple normalized sub-indicators corresponding to the robustness index.

[0055] For example, normalizing multiple sub-performance indicators corresponding to the robustness indicator of the target device respectively to obtain multiple normalized sub-indicators can include the following operations: determining the maximum sub-performance indicator and the minimum sub-performance indicator based on the multiple sub-performance indicators corresponding to the same robustness indicator of the multiple target devices; using the maximum sub-performance indicator and the minimum sub-performance indicator to normalize the multiple sub-performance indicators corresponding to the robustness indicator of the target device respectively to obtain multiple normalized sub-indicators.

[0056] For example, the maximum sub-performance indicator and the minimum sub-performance indicator corresponding to the robustness indicator of the target device may be used to normalize multiple sub-performance indicators corresponding to the robustness indicator of the target device to obtain multiple normalized sub-indicators.

[0057] For example, using the maximum sub-performance indicator and the minimum sub-performance indicator, multiple sub-performance indicators corresponding to the robustness indicator of the target device are normalized respectively to obtain multiple normalized sub-indicators, which can include the following operations: when the sub-performance indicator is a positive indicator, the normalized sub-indicator is obtained according to the ratio of the difference between the sub-performance indicator and the minimum sub-performance indicator to the difference between the maximum sub-performance indicator and the minimum sub-performance indicator, wherein the positive indicator is an indicator with a larger indicator value, the stronger the robustness; when the sub-performance indicator is a negative indicator, the normalized sub-indicator is obtained according to the ratio of the difference between the maximum sub-performance indicator and the sub-performance indicator to the difference between the maximum sub-performance indicator and the minimum sub-performance indicator, wherein the negative indicator is an indicator with a larger indicator value, the weaker the robustness.

[0058] For example, positive indicators can be throughput, the number of read and write operations per second, bandwidth, etc. Negative indicators can be latency, error rate, etc.

[0059] For example, the initial sub-performance indicator data matrix is , where M is the number of target devices and J is the total number of multiple robustness indicators. ij represents the i-th sub-performance indicator under the j-th robust performance indicator, J is an integer greater than 1, 1≤j≤J and j is an integer.

[0060] When the sub-performance indicator is a positive indicator, the normalized sub-indicator can be obtained according to formula (1).

[0061] (1).

[0062] in, is the maximum sub-performance index corresponding to the j-th robust performance index, is the minimum sub-performance index corresponding to the j-th robust performance index. ij is the normalized sub-indicator of the i-th sub-performance indicator under the j-th robust performance indicator.

[0063] When the sub-performance index is a negative index, the normalized sub-index Z can be obtained according to formula (2): ij .

[0064] (2)

[0065] According to an embodiment of the present application, when the sub-performance indicator is a positive indicator, a normalized sub-indicator is obtained based on the ratio of the difference between the sub-performance indicator and the minimum sub-performance indicator to the difference between the maximum sub-performance indicator and the minimum sub-performance indicator; and when the sub-performance indicator is a negative indicator, a normalized sub-indicator is obtained based on the ratio of the difference between the maximum sub-performance indicator and the sub-performance indicator to the difference between the maximum sub-performance indicator and the minimum sub-performance indicator, thereby unifying the data of the initial sub-performance indicators into positive indicators and eliminating the dimensional effect.

[0066] For example, normalizing the statistical information of multiple normalized sub-indicators corresponding to each of the multiple robustness indicators respectively, and determining multiple first weights may include: calculating the mean of the multiple normalized sub-indicators; calculating the standard deviation of the multiple normalized sub-indicators; obtaining the first coefficient of variation based on the standard deviation and the mean; normalizing the multiple first coefficients of variation corresponding one-to-one to the multiple robustness indicators to obtain multiple first weights.

[0067] According to an embodiment of the present application, the larger the first coefficient of variation, the greater the fluctuation in the index values of multiple normalized sub-indicators corresponding to the robustness index, the more significant the impact of the robustness index on the overall performance of the target device, and the higher the first weight should be given.

[0068] Formula (3) can be used to calculate the mean of multiple normalized sub-indicators, and formula (4) can be used to calculate the standard deviation of multiple normalized sub-indicators. Formula (5) can be used to obtain the first coefficient of variation based on the standard deviation and the mean. Formula (6) can be used to normalize the multiple first coefficients of variation that correspond one-to-one to the multiple robustness indicators to obtain multiple first weights.

[0069] (3).

[0070] in, is the mean of the multiple normalized sub-indicators corresponding to the j-th robust performance indicator, and I is the total number of the multiple normalized sub-indicators corresponding to the j-th robust performance indicator. I is an integer greater than 1, 1≤i≤I, and i is an integer.

[0071] (4).

[0072] in, is the standard deviation of multiple normalized sub-indicators corresponding to the j-th robust performance indicator.

[0073] (5).

[0074] in, is the first coefficient of variation corresponding to the j-th robust performance indicator.

[0075] (6).

[0076] in, is the first weight corresponding to the j-th robust performance indicator.

[0077] Formula (7) can be used to weight multiple robustness indicators using multiple first weights to determine the comprehensive performance evaluation value of the target device.

[0078] (7).

[0079] in, is the comprehensive performance evaluation value of the mth target device, is the j-th robust performance indicator. is a set of multiple normalized sub-indicators Z corresponding to the j-th robust performance indicator ij Obtained by weighted summation. 1≤m≤M, where m is an integer.

[0080] According to an embodiment of the present application, multiple sub-performance indicators corresponding to the robustness indicator of a target device are normalized to obtain multiple normalized sub-indicators. The statistical information of the multiple normalized sub-indicators corresponding to each of the multiple robustness indicators is normalized to determine multiple first weights. This automatically obtains multiple first weights reflecting the importance of the multiple robustness indicators. Subsequently, the multiple robustness indicators are weighted using the multiple first weights to determine a comprehensive performance evaluation value for the target device. This automatically obtains a comprehensive performance evaluation value reflecting the true comprehensive performance of the target device in real time, paving the way for the subsequent automatic identification of target devices with uneven performance and the automatic repair of the performance of target devices with uneven performance.

[0081] For example, the entropy weight method may be used to process multiple robustness indicators for multiple target devices in a server cluster to determine comprehensive performance evaluation values for each of the multiple target devices.

[0082] According to the embodiments of the present application, the entropy weight method is an objective weighting method that can determine objective weights based on the size of the indicator variability. The basic idea of the entropy weight method is: if the information entropy of a certain performance indicator is smaller, it means that the degree of variability of the performance indicator is greater, the amount of information provided is more, the role played in the comprehensive performance evaluation is greater, and its weight is greater. Conversely, the greater the information entropy of a certain performance indicator, the smaller the degree of variability of the performance indicator, the less information provided, the smaller the role played in the comprehensive performance evaluation, and the smaller its weight. Therefore, the entropy weight method can be used to calculate the weights of each performance indicator to provide a basis for the comprehensive evaluation of multi-indicator performance.

[0083] According to the embodiments of the present application, Figure 2 Operation S201 shown, determining the comprehensive performance evaluation value of each of the multiple target devices based on the multiple robustness indicators for the multiple target devices in the server cluster, may include the following operations: normalizing the multiple sub-performance indicators corresponding to the robustness indicators of the target devices to obtain multiple normalized sub-indicators; determining the information entropy corresponding to the robustness indicators of the target devices based on multiple feature proportions determined by each normalized sub-indicator and the sum of the multiple normalized sub-indicators; normalizing the information entropy corresponding to the multiple robustness indicators of the target devices to determine multiple second weights; and weighting the multiple robustness indicators using the multiple second weights to determine the comprehensive performance evaluation value of the target devices.

[0084] For example, formula (1) and formula (2) can be used to normalize multiple sub-performance indicators corresponding to the robustness indicator of the target device to obtain multiple normalized sub-indicators.

[0085] For example, formula (8) can be used to determine multiple feature weights based on the normalized sub-indicator and the sum of multiple normalized sub-indicators. Formula (9) can be used to determine the information entropy corresponding to the robustness index of the target device based on multiple feature weights determined by each normalized sub-indicator and the sum of multiple normalized sub-indicators.

[0086] (8).

[0087] in, is the feature weight of the i-th sub-performance indicator under the j-th robust performance indicator.

[0088] (9).

[0089] in, is the information entropy corresponding to the j-th robustness indicator. a is a constant. .

[0090] For example, normalizing the information entropies corresponding to the multiple robustness indicators of the target device and determining the multiple second weights may include: obtaining multiple second coefficients of variation based on the information entropies corresponding to the multiple robustness indicators of the target device and a preset value; and normalizing the multiple second coefficients of variation to obtain the multiple second weights. For example, the preset value may be 1.

[0091] Formula (10) can be used to obtain multiple second variation coefficients based on the information entropy and preset values corresponding to multiple robustness indicators of the target device.

[0092] (10).

[0093] in, is the second coefficient of variation corresponding to the j-th robustness indicator.

[0094] Formula (11) can be used to normalize multiple second coefficients of variation to obtain multiple second weights.

[0095] (11)

[0096] in, is the second coefficient of variation corresponding to the j-th robust performance indicator.

[0097] Formula (12) can be used to weight multiple robustness indicators using multiple second weights to determine the comprehensive performance evaluation value of the target device.

[0098] (12).

[0099] According to an embodiment of the present application, multiple sub-performance indicators corresponding to the robustness indicator of the target device are normalized to obtain multiple normalized sub-indicators. The information entropy corresponding to the robustness indicator of the target device is determined based on multiple feature proportions determined by each normalized sub-indicator and the sum of the multiple normalized sub-indicators. The information entropy corresponding to the multiple robustness indicators of the target device is normalized to determine multiple second weights. This enables the use of the entropy weight method to automatically obtain multiple second weights that reflect the importance of the multiple robustness indicators. Subsequently, the multiple second weights are used to weight the multiple robustness indicators to determine the comprehensive performance evaluation value of the target device. This allows the comprehensive performance evaluation value reflecting the true comprehensive performance of the target device to be automatically obtained in real time, preparing for the subsequent automatic identification of target devices with uneven performance and automatic repair of the performance of target devices with uneven performance.

[0100] According to the embodiments of the present application, Figure 2The device performance optimization method shown may further include the following operation: determining a performance deviation value of any target device based on the comprehensive performance evaluation value of any target device and the average comprehensive performance evaluation values of multiple devices.

[0101] For example, formula (13) can be used to determine the performance deviation value of any target device based on the comprehensive performance evaluation value of any target device and the average comprehensive performance evaluation value of multiple devices.

[0102] (13).

[0103] in, is the performance deviation value of the mth target device.

[0104] exist When the value is greater than the maximum range value in the preset threshold range, the performance of the mth target device is higher than the average comprehensive performance, and the performance of the mth target device needs to be adjusted. When the value is less than the minimum range value in the preset threshold range, the performance of the mth target device is lower than the average comprehensive performance, and the performance of the mth target device needs to be adjusted.

[0105] According to an embodiment of the present application, by determining the performance deviation value of any target device based on the comprehensive performance evaluation value of any target device and the average comprehensive performance evaluation value of multiple devices, and confirming that the performance of the target device is unbalanced when the performance deviation value of any target device is outside the preset threshold range, it is possible to limit the performance imbalance level of the target device that needs performance adjustment based on the average comprehensive performance of multiple devices and the preset threshold range, and automatically identify the target device with unbalanced performance in a timely manner. Subsequently, when the performance of the target device deviates from the average comprehensive performance to a certain extent and has not yet affected the overall performance of the server cluster, the performance of the target device can be optimized in a timely manner, so that the performance of the target device continues to remain balanced, avoiding the impact of the overall performance of the server cluster due to the high imbalance of the performance of the target device.

[0106] According to the embodiments of the present application, Figure 2 The operation S202 shown, determining the performance reallocation value of any target device based on the performance deviation value of any target device and the comprehensive performance evaluation value of any target device, may include the following operations: obtaining the performance compensation value of any target device based on the performance adjustment weight of any target device, the performance deviation value of any target device, the convergence coefficient and the comprehensive performance evaluation values of multiple target devices, the convergence coefficient representing the speed of achieving the performance optimization target of any target device; determining the performance reallocation value of any target device based on the performance compensation value of any target device and the comprehensive performance evaluation value of any target device.

[0107] For example, the performance adjustment weight can be determined in the following manner: the performance adjustment weight is determined based on the absolute value of the performance deviation value of any target device and a smoothing factor. The smoothing factor is used to determine the performance optimization amount of any target device.

[0108] For example, formula (14) can be used to determine the performance adjustment weight based on the absolute value of the performance deviation value of any target device and the smoothing factor.

[0109] (14).

[0110] in, Adjust the weight for the performance of the mth target device. . is the smoothing factor.

[0111] According to an embodiment of the present application, the smoothing factor You can choose according to the actual situation and there is no limitation here. is a decimal number between 0 and 1. For example, It can be 0.1, 0.3 or 0.9, etc.

[0112] According to an embodiment of the present application, the larger the value of the smoothing factor, the larger the value of the performance adjustment weight, and the larger the subsequent adjustment range of the comprehensive performance evaluation value according to the adjustment weight.

[0113] For example, formula (15) can be used to adjust the performance weight of any target device, the performance deviation value of any target device, the convergence coefficient, and the comprehensive performance evaluation values of multiple target devices to obtain the performance compensation value of any target device. Based on the performance compensation value of any target device and the comprehensive performance evaluation value of any target device, the performance redistribution value of any target device can be determined.

[0114] (15).

[0115] in, is the performance redistribution value of the mth target device, k is the convergence coefficient, is the sum of the comprehensive performance evaluation values of multiple target devices. The performance compensation value of any target device is .

[0116] According to an embodiment of the present application, the performance compensation value of any target device is obtained based on the performance adjustment weight of any target device, the performance deviation value of any target device, the convergence coefficient and the comprehensive performance evaluation values of each of the multiple target devices. The performance reallocation value of any target device is determined based on the performance compensation value of any target device and the comprehensive performance evaluation value of any target device, so as to control the performance optimization amount of any target device and the speed of achieving the performance optimization target according to the performance adjustment weight and the convergence coefficient, so that after the performance reallocation value is obtained by subsequent cyclic calculation, the performance of any target device can be adjusted and balanced based on the performance reallocation value obtained by the cyclic calculation.

[0117] According to an embodiment of the present application, the parameter mapping relationship represents a mapping relationship between a performance adjustment ratio and a plurality of configuration parameters, and the plurality of configuration parameters are configured with a plurality of priorities.

[0118] For example, after obtaining the performance adjustment ratio, the data segment in which the performance adjustment ratio is located is determined according to the parameter mapping relationship, and then the performance parameter adjustment values corresponding to the multiple configuration parameters are determined according to the data segment.

[0119] According to the embodiments of the present application, Figure 2 The operation S203 shown, based on the parameter mapping relationship, determines the performance parameter adjustment value of any target device according to the performance adjustment ratio of any target device determined by utilizing the performance reallocation value and the comprehensive performance evaluation value of any target device, and updates the configuration parameters of any target device using the performance parameter adjustment value, may include the following operations: determining the performance parameter adjustment value of any target device layer by layer in order from high to low priority, and updating the configuration parameters of any target device using the performance parameter adjustment value.

[0120] For example, determining the performance parameter adjustment value of any target device layer by layer in order from high to low priority, and using the performance parameter adjustment value to update the configuration parameters of any target device may include: based on the parameter mapping relationship, determining the t-th performance parameter adjustment value of any target device according to the t-th performance adjustment ratio of any target device, and using the t-th performance parameter adjustment value to update the t-th level configuration parameters of any target device, where t is a positive integer; after updating the t-th level configuration parameters of any target device using the t-th performance parameter adjustment value, recalculating the performance deviation value of any target device as the t+1-th performance deviation value; when the t+1-th performance deviation value is outside the preset threshold range, recalculating the performance adjustment ratio of any target device as the t+1-th performance adjustment ratio according to the t+1 performance deviation value; based on the parameter mapping relationship, determining the t+1-th performance parameter adjustment value of any target device according to the t+1-th performance adjustment ratio of any target device, and using the t+1-th performance parameter adjustment value to update the t+1-th level configuration parameters of any target device, and so on, until the performance deviation value is within the preset threshold range.

[0121] For example, the multiple configuration parameters may include at least two of the following: the number of channels used by the target device; the register parameters corresponding to the link where the target device is located; the physical layer dynamic equalizer parameters of the target device; the clock generator frequency used for the target device; and the transmission mode parameters of the target device.

[0122] For example, when multiple configuration parameters include the number of channels used by the target device, the register parameters corresponding to the link where the target device is located, the physical layer dynamic equalizer parameters of the target device, the clock generator frequency used for the target device, and the transmission mode parameters of the target device, the priorities of the number of channels used by the target device, the register parameters corresponding to the link where the target device is located, the physical layer dynamic equalizer parameters of the target device, the clock generator frequency used for the target device, and the transmission mode parameters of the target device are reduced in order.

[0123] For example, the number of channels used by a target device can be adjusted based on new performance parameters, adjusting the channel allocation strategy for the target device. For example, channels can be downgraded, the number of channels increased, and undamaged channels prioritized. This approach can be implemented by using pin configuration to reverse channels and map damaged channels to unused areas. This approach can be effective for bandwidth, IOPS, latency, and error rate.

[0124] For example, you can configure the register parameters corresponding to the link to the target device by modifying the values in the link control register and link capability register to adjust link parameters such as link width and auto-rate negotiation, thereby optimizing the performance of the target device. This method is effective for bandwidth, IOPS, and latency.

[0125] For example, by adjusting the target device's physical layer dynamic equalizer parameters, you can optimize signal quality to reduce error rates by adjusting the target device's physical layer pre-emphasis and de-emphasis, as well as the receiver equalizer parameters, to meet the new error rate performance requirements. This method is effective for error rates.

[0126] For example, in storage scenarios, you can use the IPMI (Intelligent Platform Management Interface) protocol to directly adjust target device performance parameters, bypassing the operating system. This method primarily leverages pre-configured target device performance adjustment features, such as modifying the clock generator frequency. This method is effective for bandwidth, IOPS, and latency.

[0127] For example, you can use interface protocol commands or custom vendor-specific instructions to adjust the PCIe transmission mode, such as switching from ASPM (Active State Power Management) L1 to L0, to target the target device's transmission mode parameters. Alternatively, you can update the PCIe device firmware with a firmware patch and directly write the new performance configuration table. This method works for bandwidth, IOPS, and latency.

[0128] According to the embodiments of the present application, by determining the performance parameter adjustment value for any target device in descending order of priority, and using the performance parameter adjustment value to update the configuration parameters of any target device, it is possible to update configuration parameters of different priorities step by step, thereby automatically repairing the performance of the target device through a dynamic repair strategy. This allows for dynamic adaptation to complex and changing load scenarios, optimizing performance while ensuring the stability of the server cluster.

[0129] Figure 3 A flow chart of a device performance optimization method according to another embodiment of the present application is shown.

[0130] like Figure 3 As shown, the device performance optimization method may include operations S301 to S304.

[0131] In operation S301 , comprehensive performance evaluation values of respective target devices in a server cluster are determined based on a plurality of robustness indicators for the target devices.

[0132] In operation S302, it is determined whether the performance deviation value of any target device is outside the preset threshold range. If so, operation S303 is executed. If not, the process returns to operation S301.

[0133] In operation S303, for any target device among the multiple target devices, when the performance deviation value of any target device is outside the preset threshold range, a performance reallocation value of any target device is determined according to the performance deviation value of any target device and the comprehensive performance evaluation value of any target device.

[0134] In operation S304, based on the parameter mapping relationship, a performance parameter adjustment value of any target device is determined according to the performance adjustment ratio of any target device determined using the performance reallocation value and the comprehensive performance evaluation value of any target device, and the configuration parameters of any target device are updated using the performance parameter adjustment value. Then, the process returns to operation S301.

[0135] According to an embodiment of the present application, the configuration parameters of any target device are updated using the performance parameter adjustment value to optimize the performance of any target device.

[0136] Based on the above equipment performance optimization method, this application also provides an equipment performance optimization device. Figure 4 The device is described in detail.

[0137] Figure 4 A block diagram of a device performance optimization apparatus according to an embodiment of the present application is shown.

[0138] like Figure 4 As shown, the device performance optimization apparatus 400 includes a first determination module 410 , a second determination module 420 and a parameter updating module 430 .

[0139] A first determination module 410 is configured to determine a comprehensive performance evaluation value for each of the multiple target devices in the server cluster based on multiple robustness indicators for the multiple target devices. The target devices are devices in the server cluster that communicate with the processor via a high-speed serial computer expansion bus. In one embodiment, the first determination module 410 can be configured to perform operation S201 described above and will not be further described here.

[0140] The second determination module 420 is configured to determine, for any target device among the multiple target devices, a performance reallocation value for the target device based on the performance deviation value of the target device and the comprehensive performance evaluation value of the target device, if the performance deviation value of the target device is outside a preset threshold range. The performance deviation value indicates the degree of deviation between the comprehensive performance of the target device and the average comprehensive performance of the multiple target devices. In one embodiment, the second determination module 420 may be configured to perform operation S202 described above, and will not be further described herein.

[0141] Parameter updating module 430 is configured to determine, based on the parameter mapping relationship and in accordance with a performance adjustment ratio for each target device determined using the performance reallocation value and the comprehensive performance evaluation value of each target device, a performance parameter adjustment value for each target device, and to update configuration parameters of each target device using the performance parameter adjustment value to optimize the performance of each target device. In one embodiment, parameter updating module 430 may be configured to perform operation S203 described above, and will not be further described herein.

[0142] According to embodiments of the present application, any multiple modules among the first determination module 410, the second determination module 420, and the parameter update module 430 may be combined into a single module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present application, at least one of the first determination module 410, the second determination module 420, and the parameter update module 430 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or may be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of these. Alternatively, at least one of the first determination module 410, the second determination module 420, and the parameter update module 430 may be at least partially implemented as a computer program module that, when executed, performs the corresponding functionality.

[0143] It should be noted that the equipment performance optimization device part in the embodiments of this application corresponds to the equipment performance optimization method part in the embodiments of this application. For the specific description corresponding to the equipment performance optimization device part, please refer to the equipment performance optimization method part and will not be repeated here.

[0144] Figure 5 A block diagram of an electronic device suitable for implementing the device performance optimization method described above according to an embodiment of the present application is shown. Figure 5 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0145] like Figure 5As shown, the electronic device 500 according to an embodiment of the present application includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage unit 508 into a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include onboard memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present application.

[0146] Various programs and data required for the operation of the electronic device 500 are stored in the RAM 503. The processor 501, ROM 502, and RAM 503 are connected to each other via a bus 504. The processor 501 performs various operations of the method flow according to the embodiment of the present application by executing the programs in the ROM 502 and / or RAM 503. It should be noted that the programs may also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 may also perform various operations of the method flow according to the embodiment of the present application by executing the programs stored in the one or more memories.

[0147] According to an embodiment of the present application, electronic device 500 may further include an input / output (I / O) interface 505, which is also connected to bus 504. Electronic device 500 may also include one or more of the following components connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 508 including a hard disk; and a communication section 509 including a network interface card such as a LAN card or modem. Communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 510 as needed, so that computer programs read from the removable media can be installed into storage section 508 as needed.

[0148] According to an embodiment of the present application, the method flow according to the embodiment of the present application can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above-mentioned functions defined in the system of the embodiment of the present application are executed. According to an embodiment of the present application, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0149] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of this application is implemented.

[0150] According to embodiments of the present application, a computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0151] For example, according to an embodiment of the present application, the computer-readable storage medium may include the ROM 502 and / or RAM 503 described above and / or one or more memories other than the ROM 502 and RAM 503 .

[0152] An embodiment of the present application also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present application. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the device performance optimization method provided by the embodiment of the present application.

[0153] When the computer program is executed by the processor 501, the above functions defined in the system / device of the embodiment of the present application are performed. According to the embodiment of the present application, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0154] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 509, and / or installed from a removable medium 511. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0155] According to an embodiment of the present application, the program code for executing the computer program provided by the embodiment of the present application can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, languages such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0156] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It will be understood by those skilled in the art that the features described in the various embodiments of the present application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments of the present application may be combined and / or coupled in various ways, and all of these combinations and / or couplings fall within the scope of the present application.

[0157] The embodiments of the present application have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present application. Although each embodiment has been described above separately, this does not mean that the measures in each embodiment cannot be advantageously used in combination. The scope of the present application is defined by the accompanying embodiments and their equivalents. Without departing from the scope of the present application, those skilled in the art may make various substitutions and modifications, and these substitutions and modifications should all fall within the scope of the present application.

Claims

1. A method for optimizing equipment performance, characterized in that: include: determining, based on a plurality of robustness indicators for a plurality of target devices in a server cluster, a comprehensive performance evaluation value for each of the plurality of target devices, wherein the target devices are devices in the server cluster that communicate with a processor based on a high-speed serial computer expansion bus; For any target device among the multiple target devices, when the performance deviation value of the any target device is outside a preset threshold range, obtaining a performance compensation value for the any target device based on the performance adjustment weight of the any target device, the performance deviation value of the any target device, a convergence coefficient, and the comprehensive performance evaluation values of each of the multiple target devices, wherein the convergence coefficient represents a speed at which a performance optimization target of the any target device is achieved; and determining a performance reallocation value for the any target device based on the performance compensation value of the any target device and the comprehensive performance evaluation value of the any target device, wherein the performance deviation value represents a degree of deviation between the comprehensive performance of the any target device and an average comprehensive performance of the multiple target devices; Based on the parameter mapping relationship, the performance parameter adjustment value of any target device is determined according to the performance adjustment ratio of any target device determined using the performance reallocation value and the comprehensive performance evaluation value of any target device, and the configuration parameters of any target device are updated using the performance parameter adjustment value to optimize the performance of any target device.

2. The method according to claim 1, characterized in that The performance adjustment weight is determined in the following manner: The performance adjustment weight is determined according to an absolute value of the performance deviation value of any target device and a smoothing factor, and the smoothing factor is used to determine the performance optimization amount of any target device.

3. The method according to claim 1, characterized in that The method further comprises: A performance deviation value of the any target device is determined according to the comprehensive performance evaluation value of the any target device and the average comprehensive performance evaluation values of the plurality of target devices.

4. The method according to claim 1, wherein The parameter mapping relationship represents a mapping relationship between a performance adjustment ratio and a plurality of configuration parameters, wherein the plurality of configuration parameters are configured with a plurality of priorities; The determining, based on the parameter mapping relationship, a performance parameter adjustment value of any target device according to a performance adjustment ratio of any target device determined using the performance reallocation value and a comprehensive performance evaluation value of any target device, and updating the configuration parameters of any target device using the performance parameter adjustment value includes: The performance parameter adjustment value of any target device is determined layer by layer in descending order of priority, and the configuration parameter of any target device is updated using the performance parameter adjustment value.

5. The method according to claim 4, characterized in that The multiple configuration parameters include at least two of the following: The number of channels used by the target device; Register parameters corresponding to the link where the target device is located; Physical layer dynamic equalizer parameters of the target device; a clock generator frequency for the target device; The transmission mode parameters of the target device.

6. The method according to claim 1, characterized in that Determining the comprehensive performance evaluation value of each of the plurality of target devices according to the plurality of robustness indicators for the plurality of target devices in the server cluster includes: Normalizing the multiple sub-performance indicators corresponding to the robustness indicator of the target device to obtain multiple normalized sub-indicators; Normalizing statistical information of a plurality of normalized sub-indicators corresponding to each of the plurality of robustness indicators to determine a plurality of first weights; The multiple robustness indicators are weighted using the multiple first weights to determine a comprehensive performance evaluation value of the target device.

7. The method according to claim 6, characterized in that Normalizing the multiple sub-performance indicators corresponding to the robustness indicator of the target device to obtain the multiple normalized sub-indicators includes: Determining a maximum sub-performance indicator and a minimum sub-performance indicator according to a plurality of sub-performance indicators respectively corresponding to the same robustness indicator of the plurality of target devices; The maximum sub-performance indicator and the minimum sub-performance indicator are used to respectively normalize a plurality of sub-performance indicators corresponding to the robustness indicator of the target device to obtain the plurality of normalized sub-indicators.

8. The method according to claim 7, characterized in that Normalizing the plurality of sub-performance indicators corresponding to the robustness indicator of the target device by using the maximum sub-performance indicator and the minimum sub-performance indicator to obtain the plurality of normalized sub-indicators includes: In the case where the sub-performance indicator is a positive indicator, the normalized sub-indicator is obtained according to the ratio of the difference between the sub-performance indicator and the minimum sub-performance indicator to the difference between the maximum sub-performance indicator and the minimum sub-performance indicator, wherein the positive indicator is an indicator whose larger value indicates stronger robustness; In the case where the sub-performance indicator is a negative indicator, the normalized sub-indicator is obtained based on the ratio of the difference between the maximum sub-performance indicator and the sub-performance indicator to the difference between the maximum sub-performance indicator and the minimum sub-performance indicator, wherein the negative indicator is an indicator whose robustness is weaker as the indicator value is larger.

9. The method according to claim 1, characterized in that Determining the comprehensive performance evaluation value of each of the plurality of target devices according to the plurality of robustness indicators for the plurality of target devices in the server cluster includes: Normalizing the multiple sub-performance indicators corresponding to the robustness indicator of the target device to obtain multiple normalized sub-indicators; Determining, according to a plurality of feature proportions determined by summing each of the normalized sub-indicators and the plurality of normalized sub-indicators, an information entropy corresponding to the robustness indicator of the target device; Normalizing the information entropies corresponding to the multiple robustness indicators of the target device respectively to determine multiple second weights; The multiple robustness indicators are weighted using the multiple second weights to determine a comprehensive performance evaluation value of the target device.

10. The method according to claim 1, characterized in that The multiple robustness indicators include at least two of bandwidth, number of read and write operations per second, latency and error rate.

11. A device for optimizing equipment performance, characterized in that: include: a first determining module configured to determine a comprehensive performance evaluation value of each of the plurality of target devices in the server cluster based on a plurality of robustness indicators for the plurality of target devices, wherein the target devices are devices in the server cluster that communicate with a processor based on a high-speed serial computer expansion bus; a second determining module, configured to, for any target device among the multiple target devices, obtain, if a performance deviation value of the any target device is outside a preset threshold range, a performance compensation value for the any target device based on the performance adjustment weight of the any target device, the performance deviation value of the any target device, a convergence coefficient, and comprehensive performance evaluation values of each of the multiple target devices, wherein the convergence coefficient represents a speed at which a performance optimization target of the any target device is achieved; and determine, based on the performance compensation value of the any target device and the comprehensive performance evaluation value of the any target device, a performance reallocation value for the any target device, wherein the performance deviation value represents a degree of deviation between the comprehensive performance of the any target device and an average comprehensive performance of the multiple target devices; A parameter updating module is used to determine, based on a parameter mapping relationship and in accordance with a performance adjustment ratio of any target device determined using the performance reallocation value and a comprehensive performance evaluation value of any target device, a performance parameter adjustment value of any target device, and to update a configuration parameter of any target device using the performance parameter adjustment value so as to optimize the performance of any target device.

12. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

14. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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