Method and device for adjusting performance capacity of server and electronic equipment

Abstract

The invention discloses a performance capacity adjusting method and device of a server and electronic equipment, and relates to the technical field of big data, and the adjusting method comprises the following steps: collecting current performance index data of each server, and inputting the current performance index data into a preset prediction model, obtaining a prediction result of each server in a first preset time period, comparing the performance index prediction value with a preset parameter threshold range of the performance index, and determining a resource configuration strategy of each server; and based on the resource configuration strategy, carrying out capacity expansion and shrinkage adjustment on the performance capacity of the server. According to the method and the device, the technical problem of relatively low operation and maintenance efficiency caused by incapability of performing real-time adjustment on the performance capacity of the server in related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data, and in particular, to a method and device for adjusting the performance capacity of a server, and an electronic device. Background Art

[0002] With the rapid development of institutional business, the scale and complexity of the number of servers that need to be managed in the data center are also increasing rapidly. The operation and maintenance team needs to handle not only the performance problems of individual servers, but also pay more attention to the resource balance and performance capacity optimization of the entire server cluster to ensure the effective utilization of resources and the efficient operation of the system.

[0003] However, the current operation and maintenance mode has the following problems: uneven server resources, equipment aging, long time-consuming in fault location and solution, low efficiency in resource recycling and equipment automatic replacement, and performance bottlenecks. In related technologies, generally, manual analysis is performed on performance capacity indicators such as the storage and CPU (Central Processing Unit) occupancy rate of the server, and solutions are proposed. However, this method is time-consuming and laborious, and it is difficult to achieve overall monitoring of the server cluster.

[0004] Therefore, there is an urgent need for an efficient and intelligent method for dynamic monitoring and automatic operation and maintenance of a server cluster to improve management efficiency and response speed, and optimize resource allocation and fault handling.

[0005] In response to the above problems, no effective solution has been proposed yet. Summary of the Invention

[0006] Embodiments of the present invention provide a method and device for adjusting the performance capacity of a server, and an electronic device, so as to at least solve the technical problem in related technologies that the performance capacity of the server cannot be adjusted in real time, resulting in low operation and maintenance efficiency.

[0007] According to one aspect of the embodiments of the present application, a method for adjusting the performance capacity of a server is provided, including: collecting current performance index data of each server, and inputting the current performance index data into a preset prediction model to obtain a prediction result of each server in a first preset time period, where the prediction result at least includes: usage status information of each performance index in the first preset time period; the performance indexes at least include: processor occupancy rate, memory usage rate; the usage status information includes: performance index prediction values of each performance index at each time point; comparing the performance index prediction values with a preset parameter threshold range of the performance indexes to determine a resource configuration strategy for each server; and based on the resource configuration strategy, performing scaling adjustment on the performance capacity of the server.

[0008] Further, before collecting the current performance metric data of each server and inputting the current performance metric data into a preset prediction model to obtain the prediction results of each server for the first preset time period, it further includes: collecting the historical performance metric data of each server, processing the historical performance metric data to obtain preset structured data, where the historical performance metric data corresponds to the address identifier of the server; collecting the configuration data of each server, where the configuration data corresponds to the address identifier of the server; associating the performance metric data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier.

[0009] Further, after associating the performance metric data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier, it further includes: determining a first preset curve based on the associated data, where the first preset curve is the curve of the historical performance metric values changing with time under the historical configuration of the server; in the case where it is detected that the absolute difference between two points within the first preset duration of the first preset curve is greater than the preset difference threshold, determining the historical performance metric data for the first preset duration as the first performance metric change data; in the case where it is detected that the historical performance metric values of all points within the second preset duration of the first preset curve are greater than the preset metric threshold, determining the historical performance metric data for the second preset duration as the second performance metric change data; collecting the historical scaling adjustment data, and determining the training data based on the historical scaling adjustment data, the preset structured data, the first performance metric change data, and the first performance metric change data.

[0010] Further, after determining the training data, it includes: performing smoothing processing on the training data to obtain target training data; training an initial prediction model based on the target training data to obtain a preset prediction model.

[0011] Further, the preset parameter threshold range at least includes: a maximum metric threshold and a minimum metric threshold. The step of comparing the performance metric prediction value with the preset parameter threshold range of the performance metric to determine the resource configuration strategy for each server includes: comparing the performance metric prediction value with the maximum metric threshold and comparing the performance metric prediction value with the minimum metric threshold; in the case where the performance metric prediction value is greater than or equal to the maximum metric threshold, determining an expansion strategy for the server; in the case where the performance metric prediction value is less than or equal to the minimum metric threshold, determining a contraction strategy for the server; determining the resource configuration strategy based on the expansion strategy and the contraction strategy.

[0012] Further, after associating the performance metric data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier, it further includes: generating a second preset curve based on all the associated data, where the second preset curve is a curve showing the change of the historical performance metric values of each server over time under the historical configuration of the server cluster; determining, based on the second preset curve, whether there are historical performance metric values of multiple servers greater than a preset metric threshold during a second preset time period; and adjusting the configuration data of each server in the server cluster when there are historical performance metric values of multiple servers greater than the preset metric threshold during the second preset time period.

[0013] Further, the method for adjusting the performance capacity of a server further includes: determining a server usage time threshold for each server; recording the usage duration of the server after starting the server; and replacing the server when the usage duration is greater than or equal to the server usage time threshold.

[0014] According to another aspect of the embodiments of the present application, there is also provided a device for adjusting the performance capacity of a server, including: an input unit configured to collect the current performance metric data of each server and input the current performance metric data into a preset prediction model to obtain a prediction result of each server during a first preset time period, where the prediction result at least includes: the usage status information of each performance metric during the first preset time period; the performance metrics at least include: the processor occupancy rate and the memory usage rate; the usage status information includes: the predicted performance metric values of the performance metrics at each time point; a determination unit configured to compare the predicted performance metric values with a preset parameter threshold range of the performance metrics to determine the resource configuration strategy of each server; and an adjustment unit configured to perform scaling adjustment on the performance capacity of the server based on the resource configuration strategy.

[0015] Further, the device for adjusting the performance capacity of a server includes: a first processing module configured to collect the historical performance metric data of each server and process the historical performance metric data to obtain preset structured data before collecting the current performance metric data of each server and inputting the current performance metric data into a preset prediction model to obtain a prediction result of each server during a first preset time period, where the historical performance metric data corresponds to the address identifier of the server; a first collection module configured to collect the configuration data of each server, where the configuration data corresponds to the address identifier of the server; and a first association module configured to associate the performance metric data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier.

[0016] Furthermore, the performance capacity adjustment device of the server further includes: a first determination module, configured to, after associating the performance index data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier, determine a first preset curve based on the associated data, where the first preset curve is a curve of the historical performance index values changing over time under the historical configuration of the server; a second determination module, configured to, when it is detected that the absolute difference between two points within a first preset duration of the first preset curve is greater than a preset difference threshold, determine the historical performance index data within the first preset duration as first performance index change data; a third determination module, configured to, when it is detected that all the historical performance index values within a second preset duration of the first preset curve are greater than a preset index threshold, determine the historical performance index data within the second preset duration as second performance index change data; a fourth determination module, configured to collect historical scaling adjustment data, and determine training data based on the historical scaling adjustment data, preset structured data, the first performance index change data, and the first performance index change data.

[0017] Furthermore, the performance capacity adjustment device of the server further includes: a second processing module, configured to perform smoothing processing on the training data after determining the training data to obtain target training data; a first training module, configured to train an initial prediction model based on the target training data to obtain a preset prediction model.

[0018] Furthermore, the preset parameter threshold range at least includes: a maximum index threshold and a minimum index threshold. The determination unit includes: a first comparison module, configured to compare the performance index prediction value with the maximum index threshold and compare the performance index prediction value with the minimum index threshold; a fifth determination module, configured to determine an expansion strategy for the server when the performance index prediction value is greater than or equal to the maximum index threshold; a sixth determination module, configured to determine a scaling-down strategy for the server when the performance index prediction value is less than or equal to the minimum index threshold; a seventh determination module, configured to determine a resource configuration strategy based on the expansion strategy and the scaling-down strategy.

[0019] Further, the performance capacity adjustment device of the server further includes: a first generation module, configured to generate a second preset curve based on all the associated data after associating the performance metric data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier, where the second preset curve is a curve of the historical performance metric values of each server changing over time under the historical configuration of the server cluster; a first determination module, configured to determine, based on the second preset curve, whether there are historical performance metric values of multiple servers greater than a preset metric threshold in a second preset time period; a first adjustment module, configured to adjust the configuration data of each server in the server cluster when there are historical performance metric values of multiple servers greater than the preset metric threshold in the second preset time period.

[0020] Further, the performance capacity adjustment device of the server further includes: an eighth determination module, configured to determine a server usage time threshold for each server; a first recording module, configured to record the usage duration of the server after the server is started; a first replacement module, configured to replace the server when the usage duration is greater than or equal to the server usage time threshold.

[0021] According to another aspect of the embodiments of the present application, there is also provided a computer program product, including a non-volatile computer-readable storage medium storing a computer program, where the computer program, when executed by a processor, implements the performance capacity adjustment method of any one of the above servers.

[0022] According to another aspect of the embodiments of the present application, there is also provided an electronic device, including one or more processors and a memory, where the memory is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the performance capacity adjustment method of any one of the above servers.

[0023] In the present invention, the current performance metric data of each server is collected and input into a preset prediction model to obtain the prediction result of each server in a first preset time period. The performance metric prediction value is compared with the preset parameter threshold range of the performance metric to determine the resource configuration strategy of each server, and based on the resource configuration strategy, the performance capacity of the server is adjusted for expansion or contraction, solving the technical problem in the related art that the performance capacity of the server cannot be adjusted in real time, resulting in low operation and maintenance efficiency.

[0024] In the present invention, by collecting the current performance metric data of each server and inputting the current performance metric data into a preset prediction model, the predicted values of the performance metrics of each server for each performance metric over a first preset time period can be obtained. Subsequently, by comparing the predicted values of the performance metrics with the preset parameter threshold range of the performance metrics, the resource configuration strategy for each server can be determined. Then, based on the resource configuration strategy, the performance capacity of the server can be adjusted for expansion or contraction, which can improve the accuracy of the server configuration strategy, reduce redundant storage occupancy, ensure the health of the server performance capacity, achieve automatic real-time adjustment of the server performance capacity, reduce labor costs, and thus achieve the technical effect of improving the operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0026] Figure 1 shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for adjusting the performance capacity of a server;

[0027] Figure 2 is a flowchart of a method for adjusting the performance capacity of a server according to Embodiment 1 of the present application;

[0028] Figure 3 is a schematic diagram of an optional device for adjusting the performance capacity of a server according to an embodiment of the present application;

[0029] Figure 4 is a structure block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] In order to enable those skilled in the art of the present technology to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0032] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) collected and involved in the present invention are all information and data authorized by the user or fully authorized by all parties. Moreover, the processing of relevant data such as collection, storage, use, processing, transmission, provision, disclosure and application complies with the relevant laws, regulations and standards of the relevant regions, takes necessary confidentiality measures, does not violate public order and good customs, and provides corresponding operation entrances for users to choose to authorize or refuse. For example, there is an interface between the present system and relevant users or institutions. Before obtaining relevant information, a request for acquisition needs to be sent to the aforementioned users or institutions through the interface, and after receiving the consent information feedback from the aforementioned users or institutions, the relevant information is obtained.

[0033] In the present invention, by collecting, extracting and predicting and analyzing the key performance capacity indicators of the server, automatic scaling can be achieved to ensure the health of the server performance capacity. By automatically optimizing the configuration data through artificial intelligence machine learning, the time for manual processing is effectively reduced. Moreover, the server configuration can be elastically scaled according to the dynamic curve, improving the accuracy of the server configuration strategy, reducing the risk of production failure events. At the same time, the characteristics of the cloud computing server can be utilized to automatically synchronize the decisions of the same cluster, automatically improve the server architecture management, and achieve the unified planning and management of large-scale servers.

[0034] The present invention will be described in detail below in conjunction with various embodiments.

[0035] Embodiment 1

[0036] According to an embodiment of the present application, an embodiment of a method for adjusting the performance capacity of a server is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0037] The method embodiment provided by the first embodiment of the present application can be executed in a mobile terminal, a computer terminal, or a similar computing device. Figure 1 The following shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing the method for adjusting the performance capacity of a server. As Figure 1 shown, the computer terminal 10 (or mobile device) may include one or more ( Figure 1 shown as 102a, 102b,..., 102n in the figure) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may further include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Among them, the network interface can be connected to a wired and / or wireless network. 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 electronic device. For example, the computer terminal 10 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.

[0038] It should be noted that the above one or more processors 102 and / or other data processing circuits are generally referred to as "data processing circuits" in this article. The data processing circuit can be embodied in software, hardware, firmware, or any combination thereof in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of the other elements in the computer terminal 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is a kind of processor control (such as the selection of a variable resistance terminal path connected to an interface).

[0039] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the server performance capacity adjustment method in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the above-mentioned server performance capacity adjustment method. The memory 104 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 memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the computer terminal 10 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.

[0040] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0041] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables a user to interact with the user interface of the computer terminal 10 (or mobile device).

[0042] Under the above operating environment, the present application provides a server performance capacity adjustment method as Figure 2 shown. Figure 2 is a flowchart of the server performance capacity adjustment method according to Embodiment 1 of the present application, as Figure 2 shown, and the method includes the following steps:

[0043] Step S201, collect the current performance index data of each server, and input the current performance index data into a preset prediction model to obtain the prediction result of each server in the first preset time period, where the prediction result at least includes: the usage status information of each performance index in the first preset time period; the performance indexes at least include: processor occupancy rate, memory usage rate; the usage status information includes: the performance index prediction value of each performance index at each time point.

[0044] Optionally, software components (such as an agent for collecting and monitoring system data) can be deployed on the application server to collect operating system log data. By processing the log information collected by the agent, performance metric data can be obtained.

[0045] In an embodiment of the present invention, the currently collected performance metric data of each server (such as an application server, a database server, etc.) is input into a preset prediction model, and the prediction results of each performance metric of each server in a first preset time period (i.e., a period of time in the future, such as the next few minutes, hours, or days) can be obtained (such as the occupancy rate of the processor, the usage rate of the memory).

[0046] Step S202: Compare the predicted performance metric values with the preset parameter threshold range of the performance metric to determine the resource configuration strategy for each server.

[0047] Optionally, a preset parameter threshold (such as the occupancy rate threshold of the processor) range of the performance metric can be set, and the predicted performance metric values are compared with this preset parameter threshold range.

[0048] In an embodiment of the present invention, according to the comparison result between the predicted performance metric values and the preset parameter threshold range, the resource configuration strategy for each server (such as an expansion strategy, a contraction strategy, etc.) can be obtained. For example, when the occupancy rate of the processor (i.e., the CPU occupancy rate) is higher than the preset parameter threshold (such as 90%), the expansion strategy can be executed.

[0049] Step S203: Based on the resource configuration strategy, perform expansion or contraction adjustment on the performance capacity of the server.

[0050] Optionally, according to the resource configuration strategy, the performance capacity of the server can be expanded or contracted. For example, according to the expansion strategy (including the type and quantity of resources that need to be additionally allocated to the server), the performance capacity of the server can be expanded (such as increasing the memory space, etc.), and according to the contraction strategy (including the type and quantity of resources that need to be reduced from the server), the performance capacity of the server can be contracted (such as reducing the memory space, etc.).

[0051] In summary, by inputting the currently collected performance metric data of each server into a preset prediction model, the predicted performance metric values of each performance metric of each server in a first preset time period can be obtained. By comparing the predicted performance metric values with the preset parameter threshold range of the performance metric, the resource configuration strategy for each server can be determined. According to the resource configuration strategy, the performance capacity of the server is expanded or contracted, thereby solving the technical problem in the related art that the performance capacity of the server cannot be adjusted in real time, resulting in low operation and maintenance efficiency.

[0052] In order to accurately obtain the associated data of the server indicated by the address identifier, in the server performance capacity adjustment method provided in Embodiment 1 of the present application, historical performance index data of each server is collected, and the historical performance index data is processed to obtain preset structured data, where the historical performance index data corresponds to the address identifier of the server; configuration data of each server is collected, where the configuration data corresponds to the address identifier of the server; the performance index data and the configuration data corresponding to the same address identifier are associated to obtain the associated data of the server indicated by the address identifier.

[0053] Optionally, a data processing tool (such as a data warehouse tool, etc.) can be used to process the historical performance index data of each server collected to obtain preset structured data (i.e., the processed historical performance index data), and the preset structured data and the IP (Internet Protocol) data of the server can be incorporated into an IT (Information Technology) data pool (i.e., a database for centrally storing and managing various data generated in the IT environment).

[0054] In an embodiment of the present invention, configuration data of each server in the configuration management database can be collected. The configuration data includes, but is not limited to: the address identifier of the server. The performance index data and the configuration data corresponding to the address identifier of the same server can be associated to obtain the associated data of the server indicated by the address identifier.

[0055] In order to accurately determine the training data, in the server performance capacity adjustment method provided in Embodiment 1 of the present application, based on the associated data, a first preset curve is determined, where the first preset curve is a curve of the historical performance index value changing with time under the historical configuration of the server; in the case where it is detected that the absolute difference between two points within a first preset duration of the first preset curve is greater than a preset difference threshold, the historical performance index data on the first preset duration is determined as first performance index change data; in the case where it is detected that the historical performance index values of all points within a second preset duration of the first preset curve are greater than a preset index threshold, the historical performance index data on the second preset duration is determined as second performance index change data; historical scaling adjustment data is collected, and based on the historical scaling adjustment data, the preset structured data, the first performance index change data, and the first performance index change data, the training data is determined.

[0056] Optionally, physical resource metrics such as the storage status and CPU usage of the server can be used as the characteristics of application resource consumption, and combined with the time dimension to display the peak-valley curve of server resources, realizing the application resource portrait, so as to intuitively reflect the consumption status of server resources and provide data support for resource optimization, fault prediction, and automated operation and maintenance.

[0057] In the embodiment of the present invention, according to the associated data (such as performance metrics such as server storage status and CPU status), the first preset curve (that is, the curve of the historical performance metric values changing with time under the historical configuration of the server (which can be determined by the configuration data of the server)) can be determined. When it is detected that the absolute difference between two points within the first preset duration (that is, a very short time, such as 1 second) of the first preset curve is greater than the preset difference threshold (that is, a relatively large value), the historical performance metric data within the first preset duration can be determined as the first performance metric change data (that is, spike data, such as data with suddenly very high or very small performance metric values in a smooth curve); when it is detected that the historical performance metric values of all points within the second preset duration (that is, a continuous time period, such as 10 minutes) of the first preset curve are greater than the preset metric threshold (that is, the preset metric threshold, which can be determined according to the running state of the server), the historical performance metric data within the second preset duration can be determined as the second performance metric change data (that is, over-threshold data, such as data that exceeds the preset metric threshold for a continuous period of time in the first preset curve).

[0058] In the embodiment of the present invention, historical scaling adjustment data (that is, data recorded each time the server scales) can also be collected, and based on the historical scaling adjustment data, preset structured data, the first performance metric change data, and the first performance metric change data, the training data of the preset prediction model can be obtained.

[0059] In the embodiment of the present invention, the first performance metric change data and the second performance metric change data can also be integrated into the specified data format of the monitoring and alarm platform and sent to the monitoring and alarm platform for real-time alarm, so that relevant personnel can check whether there are special abnormal situations according to the alarm information.

[0060] In order to accurately obtain the preset prediction model, in the server performance capacity adjustment method provided in Embodiment 1 of the present application, the training data is smoothed to obtain the target training data; based on the target training data, the initial prediction model is trained to obtain the preset prediction model.

[0061] Optionally, the data after smoothing can better reflect the long-term trend and periodicity of the data, and at the same time can reduce the influence of short-term fluctuations and outliers.

[0062] In an embodiment of the present invention, the SMA (Simple Moving Average) function and the EMA (Exponential Moving Average) function can be used to smooth the training data, extract the trend and periodic characteristics of the training data, obtain the target training data, and train the initial prediction model according to the target training data to obtain a preset prediction model (i.e., a time series prediction model).

[0063] In order to accurately determine the resource allocation strategy, in the server performance capacity adjustment method provided in Embodiment 1 of the present application, the predicted value of the performance metric is compared with the maximum metric threshold, and the predicted value of the performance metric is compared with the minimum metric threshold; when the predicted value of the performance metric is greater than or equal to the maximum metric threshold, a server expansion strategy is determined; when the predicted value of the performance metric is less than or equal to the minimum metric threshold, a server contraction strategy is determined; based on the expansion strategy and the contraction strategy, the resource allocation strategy is determined.

[0064] Optionally, the predicted value of the performance metric can be compared with the maximum metric threshold. When the predicted value of the performance metric is greater than or equal to the maximum metric threshold (such as 90%), a server expansion strategy (i.e., a resource allocation strategy for increasing the server) is determined, and the predicted value of the performance metric is compared with the minimum metric threshold. When the predicted value of the performance metric is less than or equal to the minimum metric threshold (such as 30%), a server contraction strategy (i.e., a resource allocation strategy for reducing the server) is determined.

[0065] In an embodiment of the present invention, by setting the maximum and minimum values of the metric threshold and comparing the relationship between the maximum and minimum values of the metric threshold and the prediction result, the automatic scaling strategy of the server in the first preset time period can be determined, and based on the scaling strategy, the resource allocation strategy can be determined.

[0066] In an embodiment of the present invention, the elastic scaling ability of the server in the cloud environment can be utilized to enable the servers in the same cluster, the same high-availability mode (i.e., the server is configured with high availability), and the same application (i.e., the server serves the same application program or service) to synchronize the above resource allocation strategy.

[0067] In order to accurately adjust the configuration data of each server in the server cluster, in the server performance capacity adjustment method provided in Embodiment 1 of this application, a second preset curve is generated based on all associated data, where the second preset curve is a curve showing the change over time of the historical performance metric values of each server under the historical configuration of the server cluster; based on the second preset curve, it is determined whether there are historical performance metric values of multiple servers greater than a preset metric threshold during a second preset time period; in the case where there are historical performance metric values of multiple servers greater than the preset metric threshold during the second preset time period, the configuration data of each server in the server cluster is adjusted.

[0068] Optionally, a dynamic view (i.e., the second preset curve) can be generated according to all associated data according to a time trend or a periodic trend. According to the second preset curve, it can be determined whether there are historical performance metric values of multiple servers greater than a preset metric threshold (such as a CPU occupancy rate of 90%) during a second preset time period (i.e., a preset time period, such as a certain time period on the second preset curve). In the case where there are historical performance metric values of multiple servers greater than the preset metric threshold during the second preset time period, the configuration data of each server in the server cluster can be adjusted.

[0069] Exemplarily, the peak and valley data of the performance metrics of each server in the second preset curve can be extracted. If the performance metrics of application server A and application server B in the cluster have had peaks in history (such as a CPU occupancy rate reaching 90%), and the server is expanded according to this peak, risk warning can be given to application server C in the cluster, and application server C can also be expanded.

[0070] In an embodiment of the present invention, according to the second preset curve, it can also be determined whether the performance capacity metric value is long-term lower than a set minimum value (i.e., the minimum metric threshold). If the performance capacity metric is long-term lower than the set minimum value, part of the server with too high configuration can be recycled (such as reducing the number of CPU cores and storage size) to achieve resource optimization and cost savings.

[0071] In order to improve the accuracy of performance capacity adjustment, in the server performance capacity adjustment method provided in Embodiment 1 of this application, a server usage time threshold for each server is determined; after the server is started, the usage duration of the server is recorded; in the case where the usage duration is greater than or equal to the server usage time threshold, the server is replaced.

[0072] Optionally, using the automatic deployment ability of cloud computing, new devices can be quickly built and configured to ensure that the performance and stability of the new devices meet the business requirements.

[0073] In an embodiment of the present invention, a usage time threshold of the server can be set. After the server is started, the server can be automatically inspected, and the usage duration of the server can be recorded. When the usage duration is greater than or equal to the usage time threshold of the server, the cloud computing platform is utilized to automatically replace old equipment and automatically set up a new server.

[0074] In some optional embodiments, key factors such as the security level of the application registered in the configuration management database (i.e., the software system or service running on the server cluster or data center), resource domain division, disaster recovery isolation policy, and load conditions can also be combined with the experience and practical content in the knowledge base (such as industry documents) to compare with the applications of the same level in the current architecture management platform, automatically recommend highly available deployments and load balancing configurations, and analyze and optimize the current architecture (for example, if the knowledge base shows that the performance and stability of security applications of the same level have been significantly improved after adopting a specific load balancing strategy, then this strategy can be used for the applications in the current architecture).

[0075] The method for adjusting the performance capacity of the server provided by the embodiment of the present application can perform dynamic monitoring and analysis of the server performance capacity indicators by associating the processed performance index data with the configuration data in the configuration management database to obtain associated data, generating a first preset curve according to the associated data, realizing an application resource portrait, and automatically predicting the future peak situation of the performance capacity indicators in combination with machine learning technology. Then, by setting parameter thresholds and comparing with the prediction results, automatic scaling of the server can be achieved. At the same time, retrospective analysis can be performed according to the second preset curve to automatically adjust the configuration data of the servers in the same cluster and automatically replace old equipment, which can save costs, realize automatic adjustment of the server performance capacity and synchronization of decisions for the same cluster, contribute to optimizing system resources, and improve the efficiency of system operation and maintenance.

[0076] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0077] Embodiment 2

[0078] The embodiment of the present application also provides a device for adjusting the performance capacity of a server. It should be noted that the device for adjusting the performance capacity of the server in the embodiment of the present application can be used to execute the method for adjusting the performance capacity of the server provided by the embodiment of the present application. The following introduces the device for adjusting the performance capacity of the server provided by the embodiment of the present application.

[0079] According to the embodiment of the present application, a device for implementing the above method for adjusting the performance capacity of a server is also provided.Figure 3 It is a schematic diagram of an optional server performance capacity adjustment device according to an embodiment of the present application. As Figure 3 shown, the server performance capacity adjustment device may include: an input unit 30, a determination unit 31, and an adjustment unit 32.

[0080] Among them, the input unit 30 is used to collect the current performance index data of each server, and input the current performance index data into a preset prediction model to obtain the prediction result of each server in the first preset time period. The prediction result at least includes: the usage status information of each performance index in the first preset time period; the performance indexes at least include: the processor occupancy rate and the memory usage rate; the usage status information includes: the performance index prediction value of the performance index at each time point;

[0081] The determination unit 31 is used to compare the performance index prediction value with the preset parameter threshold range of the performance index to determine the resource configuration strategy of each server;

[0082] The adjustment unit 32 is used to perform scaling adjustment on the performance capacity of the server based on the resource configuration strategy.

[0083] The server performance capacity adjustment device provided by the embodiment of the present application can collect the current performance index data of each server through the input unit 30, input the current performance index data into a preset prediction model to obtain the prediction result of each server in the first preset time period, and can compare the performance index prediction value with the preset parameter threshold range of the performance index through the determination unit 31 to determine the resource configuration strategy of each server, and can perform scaling adjustment on the performance capacity of the server based on the resource configuration strategy through the adjustment unit 32.

[0084] Optionally, the server performance capacity adjustment device includes: a first processing module, which is used to collect the historical performance index data of each server and process the historical performance index data to obtain preset structured data before collecting the current performance index data of each server and inputting the current performance index data into a preset prediction model to obtain the prediction result of each server in the first preset time period. The historical performance index data corresponds to the address identifier of the server; a first collection module, which is used to collect the configuration data of each server, and the configuration data corresponds to the address identifier of the server; a first association module, which is used to associate the performance index data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier.

[0085] Optionally, the performance capacity adjustment device of the server further includes: a first determination module, configured to, after associating the performance index data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier, determine a first preset curve based on the associated data, where the first preset curve is a curve of the historical performance index value changing with time under the historical configuration of the server; a second determination module, configured to, when it is detected that the absolute difference between two points within a first preset duration of the first preset curve is greater than a preset difference threshold, determine the historical performance index data within the first preset duration as first performance index change data; a third determination module, configured to, when it is detected that the historical performance index values of all points within a second preset duration of the first preset curve are greater than a preset index threshold, determine the historical performance index data within the second preset duration as second performance index change data; a fourth determination module, configured to collect historical scaling adjustment data, and determine training data based on the historical scaling adjustment data, preset structured data, the first performance index change data, and the first performance index change data.

[0086] Optionally, the performance capacity adjustment device of the server further includes: a second processing module, configured to, after determining the training data, perform smoothing processing on the training data to obtain target training data; a first training module, configured to train an initial prediction model based on the target training data to obtain a preset prediction model.

[0087] Optionally, the preset parameter threshold range at least includes: a maximum index threshold and a minimum index threshold. The determination unit includes: a first comparison module, configured to compare the performance index prediction value with the maximum index threshold and compare the performance index prediction value with the minimum index threshold; a fifth determination module, configured to determine an expansion strategy for the server when the performance index prediction value is greater than or equal to the maximum index threshold; a sixth determination module, configured to determine a scaling-down strategy for the server when the performance index prediction value is less than or equal to the minimum index threshold; a seventh determination module, configured to determine a resource configuration strategy based on the expansion strategy and the scaling-down strategy.

[0088] Optionally, the performance capacity adjustment device of the server further includes: a first generation module, configured to, after associating the performance metric data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier, generate a second preset curve based on all the associated data, where the second preset curve is a curve of the historical performance metric values of each server changing with time under the historical configuration of the server cluster; a first determination module, configured to determine, based on the second preset curve, whether there are historical performance metric values of multiple servers greater than a preset metric threshold in a second preset time period; a first adjustment module, configured to, when there are historical performance metric values of multiple servers greater than the preset metric threshold in the second preset time period, adjust the configuration data of each server in the server cluster.

[0089] Optionally, the performance capacity adjustment device of the server further includes: an eighth determination module, configured to determine a server usage time threshold for each server; a first recording module, configured to record the usage duration of the server after the server is started; a first replacement module, configured to replace the server when the usage duration is greater than or equal to the server usage time threshold.

[0090] The above-mentioned performance capacity adjustment device of the server may further include a processor and a memory. The above-mentioned input unit 30, determination unit 31, adjustment unit 32, etc. are all stored in the memory as program units, and the corresponding functions are implemented by the processor executing the above-mentioned program units stored in the memory.

[0091] The above-mentioned processor includes a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and by adjusting the kernel parameters, the performance capacity of the server can be scaled up or down based on the resource configuration policy.

[0092] The above-mentioned memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

[0093] It should be noted here that the above-mentioned input unit 30, determination unit 31, adjustment unit 32 correspond to steps S201 to S203 in Embodiment 1. The examples and application scenarios implemented by the above-mentioned units and the corresponding steps are the same, but are not limited to the content disclosed in the above-mentioned Embodiment 1. It should be noted that the above-mentioned units can be hardware components or software components stored in a memory (for example, memory 104) and processed by one or more processors (for example, processors 102a, 102b,..., 102n), and the above-mentioned units can also be part of the device and can run in the computer terminal 10 provided in Embodiment 1.

[0094] Embodiment 3

[0095] An embodiment of the present application may provide a computer terminal, and the computer terminal may be any one of the computer terminal devices in a computer terminal group. Optionally, in this embodiment, the above computer terminal may also be replaced with a mobile terminal or other terminal devices such as an electronic device.

[0096] Optionally, in this embodiment, the above computer terminal may be located in at least one of multiple network devices in a computer network.

[0097] In this embodiment, the above computer terminal may execute the program code of the following steps in the method for adjusting the performance capacity of a server: collect the current performance index data of each server, and input the current performance index data into a preset prediction model to obtain the prediction results of each server in the first preset time period, where the prediction results at least include: the usage status information of each performance index in the first preset time period; the performance indexes at least include: the processor occupancy rate, the memory usage rate; the usage status information includes: the predicted values of the performance indexes at each time point; compare the predicted values of the performance indexes with the preset parameter threshold range of the performance indexes to determine the resource configuration strategy of each server; based on the resource configuration strategy, perform scaling adjustment on the performance capacity of the server.

[0098] Optionally, the above computer terminal may execute the program code of the following steps in the method for adjusting the performance capacity of a server: collect the historical performance index data of each server, and process the historical performance index data to obtain preset structured data, where the historical performance index data corresponds to the address identifier of the server; collect the configuration data of each server, where the configuration data corresponds to the address identifier of the server; associate the performance index data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier.

[0099] Optionally, the above computer terminal may execute the program code of the following steps in the server performance capacity adjustment method: determining a first preset curve based on associated data, where the first preset curve is a curve of historical performance metric values changing over time under the historical configuration of the server; in the case where it is detected that the absolute difference between two points within a first preset duration of the first preset curve is greater than a preset difference threshold, determining the historical performance metric data within the first preset duration as first performance metric change data; in the case where it is detected that the historical performance metric values of all points within a second preset duration of the first preset curve are greater than a preset metric threshold, determining the historical performance metric data within the second preset duration as second performance metric change data; collecting historical scaling adjustment data, and determining training data based on the historical scaling adjustment data, preset structured data, first performance metric change data, and second performance metric change data.

[0100] Optionally, the above computer terminal may execute the program code of the following steps in the server performance capacity adjustment method: performing smoothing processing on the training data to obtain target training data; training an initial prediction model based on the target training data to obtain a preset prediction model.

[0101] Optionally, the above computer terminal may execute the program code of the following steps in the server performance capacity adjustment method: comparing the performance metric prediction value with a maximum metric threshold and comparing the performance metric prediction value with a minimum metric threshold; in the case where the performance metric prediction value is greater than or equal to the maximum metric threshold, determining an expansion strategy for the server; in the case where the performance metric prediction value is less than or equal to the minimum metric threshold, determining a contraction strategy for the server; determining a resource allocation strategy based on the expansion strategy and the contraction strategy.

[0102] Optionally, the above computer terminal may execute the program code of the following steps in the server performance capacity adjustment method: generating a second preset curve based on all associated data, where the second preset curve is a curve of historical performance metric values of each server changing over time under the historical configuration of the server cluster; based on the second preset curve, determining whether there are historical performance metric values of multiple servers greater than a preset metric threshold within a second preset time period; in the case where there are historical performance metric values of multiple servers greater than a preset metric threshold within the second preset time period, adjusting the configuration data of each server in the server cluster.

[0103] Optionally, the above computer terminal may execute the program code of the following steps in the server performance capacity adjustment method: determining a server usage time threshold for each server; after starting the server, recording the usage duration of the server; in the case where the usage duration is greater than or equal to the server usage time threshold, replacing the server.

[0104] Optionally, Figure 4 is a structural block diagram of an electronic device according to an embodiment of the present application. As Figure 4 shown, the electronic device may include: one or more ( Figure 4 only one is shown in the figure) processors 402, a memory 404, a storage controller, and a peripheral interface, where the peripheral interface is connected to a radio frequency module, an audio module, and a display.

[0105] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the server performance capacity adjustment method and device in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the above-mentioned server performance capacity adjustment method. The memory 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 memories, or other non-volatile solid-state memories. In some instances, the memory may further include a memory remotely set relative to the processor, 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, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0106] The processor can call the information and application programs stored in the memory through a transmission device to execute the above steps in the above-mentioned server performance capacity adjustment method.

[0107] By adopting the embodiments of the present application, a solution for a server performance capacity adjustment method is provided. By collecting server performance metric data and data in a configuration management database and correlating them, associated data can be obtained. According to the associated data, an application resource portrait can be realized. According to the resource portrait, dynamic monitoring and analysis can be performed on the abnormal growth of performance metric data, and the server types (such as application servers, database servers, etc.) can be distinguished. At the same time, automatic scaling of the server can be realized according to a preset prediction model to cope with extreme situations where the server becomes unavailable due to the high-speed growth of performance metric data or the server resources are wasted due to the continuous decrease of performance metric data, realizing the automatic adjustment of performance capacity and the decision synchronization of servers in the same cluster, reducing the labor cost, and thus solving the technical problem in the related art that the server performance capacity cannot be adjusted in real time, resulting in low operation and maintenance efficiency.

[0108] Those of ordinary skill in the art can understand that Figure 4 the structure shown is only schematic, and the electronic device may also be a terminal device such as a smart phone, a tablet computer, a palm computer, and a mobile Internet device (MID). Figure 4 It does not limit the structure of the above-mentioned electronic device. For example, the electronic device may further include more thanFigure 4 more or fewer components shown therein (such as network interfaces, display devices, etc.), or having a configuration different from that Figure 4 shown.

[0109] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium. The storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, an optical disc, etc.

[0110] Embodiment 4

[0111] An embodiment of the present application also provides a storage medium. Optionally, in this embodiment, the above storage medium may be used to store the program code executed by the server performance capacity adjustment method provided in the above Embodiment 1.

[0112] Optionally, in this embodiment, the above storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0113] The present application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program for the steps of the server performance capacity adjustment method.

[0114] The serial numbers of the above embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0115] In the above embodiments of the present application, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0116] In the several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the units or modules can be in electrical or other forms.

[0117] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0118] In addition, in each embodiment of the present application, each functional unit may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0119] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs and other various media that can store program codes.

[0120] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for adjusting the performance capacity of a server, characterized in that: include: Collecting current performance indicator data of each server, and inputting the current performance indicator data into a preset prediction model, obtaining a prediction result of each server in a first preset time period, wherein the prediction result at least includes: usage status information of each performance indicator in the first preset time period; the performance indicators at least include: processor occupancy rate, memory usage rate; the usage status information includes: performance indicator prediction value of the performance indicator at each time point; Compare the predicted value of the performance indicator with the preset parameter threshold range of the performance indicator to determine the resource allocation strategy of each of the servers; Based on the resource allocation strategy, the performance capacity of the server is expanded or reduced.

2. The performance capacity adjustment method according to claim 1, characterized in that: Before collecting current performance indicator data of each server and inputting the current performance indicator data into a preset prediction model to obtain a prediction result of each server in a first preset time period, the method further includes: Collecting historical performance indicator data of each of the servers, and processing the historical performance indicator data to obtain preset structured data, wherein the historical performance indicator data corresponds to an address identifier of the server; Collecting configuration data of each of the servers, wherein the configuration data corresponds to the address identifier of the server; The performance indicator data and the configuration data corresponding to the same address identifier are associated to obtain the associated data of the server indicated by the address identifier.

3. The performance capacity adjustment method according to claim 2, characterized in that: After associating the performance indicator data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier, the method further includes: Based on the associated data, determining a first preset curve, wherein the first preset curve is a curve showing changes in historical performance indicator values ​​over time under historical configurations of the server; When it is detected that the absolute difference between two points of the first preset curve within the first preset time period is greater than a preset difference threshold, the historical performance indicator data over the first preset time period is determined as the first performance indicator change data; In the case where it is detected that the historical performance indicator values ​​of all points of the first preset curve within the second preset time period are greater than the preset indicator threshold, the historical performance indicator data over the second preset time period is determined as the second performance indicator change data; Historical expansion and contraction adjustment data is collected, and training data is determined based on the historical expansion and contraction adjustment data, the preset structured data, the first performance indicator change data, and the first performance indicator change data.

4. The performance capacity adjustment method according to claim 3, characterized in that: After determining the training data, including: Performing smoothing on the training data to obtain target training data; Based on the target training data, the initial prediction model is trained to obtain the preset prediction model.

5. The performance capacity adjustment method according to claim 1, characterized in that: The preset parameter threshold range includes at least: a maximum indicator threshold and a minimum indicator threshold. The step of comparing the performance indicator prediction value with the preset parameter threshold range of the performance indicator to determine the resource configuration strategy of each server includes: Comparing the predicted performance indicator value with the maximum indicator threshold, and comparing the predicted performance indicator value with the minimum indicator threshold; When the predicted value of the performance indicator is greater than or equal to the maximum indicator threshold, determining a capacity expansion strategy for the server; When the predicted value of the performance indicator is less than or equal to the minimum indicator threshold, determining a capacity reduction strategy for the server; The resource allocation strategy is determined based on the capacity expansion strategy and the capacity reduction strategy.

6. The performance capacity adjustment method according to claim 2, characterized in that: After associating the performance indicator data and the configuration data corresponding to the same address identifier to obtain the associated data of the server indicated by the address identifier, the method further includes: Based on all the associated data, a second preset curve is generated, wherein the second preset curve is a curve showing changes in the historical performance indicator value of each server over time under the historical configuration of the server cluster; Based on the second preset curve, determining whether the historical performance indicator values ​​of multiple servers are greater than a preset indicator threshold value in a second preset time period; In the case that the historical performance indicator values ​​of multiple servers in the second preset time period are all greater than the preset indicator threshold, the configuration data of each server in the server cluster is adjusted.

7. The performance capacity adjustment method according to claim 1, characterized in that: The performance capacity adjustment method further includes: determining a server usage time threshold for each of the servers; After starting the server, recording the usage time of the server; When the usage time is greater than or equal to the server usage time threshold, the server is replaced.

8. A performance capacity adjustment device for a server, characterized in that: include: An input unit, used to collect current performance indicator data of each server, and input the current performance indicator data into a preset prediction model to obtain a prediction result of each server in a first preset time period, wherein the prediction result at least includes: usage status information of each performance indicator in the first preset time period; the performance indicators at least include: processor occupancy rate, memory usage rate; the usage status information includes: performance indicator prediction value of the performance indicator at each time point; A determination unit, configured to compare the predicted value of the performance indicator with a preset parameter threshold range of the performance indicator to determine a resource configuration strategy for each of the servers; An adjustment unit is used to adjust the performance capacity of the server based on the resource configuration strategy.

9. A computer program product, characterized in that The invention comprises a non-volatile computer-readable storage medium storing a computer program, wherein the computer program implements the performance capacity adjustment method according to any one of claims 1 to 7 when executed by a processor.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the performance capacity adjustment method described in any one of claims 1 to 7.

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