Cost optimization method, system, device and product based on cloud resource configuration

Abstract

The invention belongs to the technical field of cloud computing, and aims to provide a cost optimization method, system, equipment and product based on cloud resource configuration. The method comprises the following steps: acquiring preprocessed cloud resource use data of each cloud service instance, and performing clustering analysis processing on the preprocessed cloud resource use data to obtain k clustering results; performing regression analysis processing on the preprocessed cloud resource usage data in each clustering result to obtain a resource usage amount prediction value corresponding to each clustering result; performing time sequence prediction processing on the resource usage amount prediction value corresponding to each clustering result to obtain a resource usage amount prediction value corresponding to each clustering result in a future specified time period; and performing cloud resource configuration on the cloud service instances in the clustering results based on the resource usage amount predicted values corresponding to the clustering results in the specified time period in the future. According to the invention, intelligent and automatic management of cloud resources can be realized, and the balance between the resource utilization rate and the cost can be ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud computing, and specifically relates to a cost optimization method, system, device and product based on cloud resource configuration. Background Art

[0002] With the rapid development of cloud computing technology, more and more enterprises and institutions rely on cloud services to support their core businesses in order to achieve flexible resource management and efficient operations. At present, cloud service providers usually use static and manual resource configuration methods to meet user needs. These methods usually rely on experience and are based on pre-set rules or policies. When creating a cloud service instance, resources are allocated according to the user's initial needs. Manual configuration methods further require administrators to perform continuous monitoring and adjustments.

[0003] However, in the process of using the prior art, the inventors found that the prior art has at least the following problems: Workloads in cloud environments are often highly dynamic and uncertain, especially under burst traffic. Existing static and manual resource configurations often cannot respond to changes in resource demand in a timely manner. If resources are over-allocated, some resources will be idle and resource utilization will be too low, which will increase unnecessary operating costs for enterprises. If resource allocation is reduced to reduce costs, critical applications may face resource shortages during peak hours, affecting service performance and user experience. Therefore, how to ensure high resource utilization while reducing costs as much as possible is a problem that is difficult to effectively solve with current technology. Summary of the invention

[0004] The present invention aims to solve the above technical problems at least to a certain extent. The present invention provides a cost optimization method, system, device and product based on cloud resource configuration.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a cost optimization method based on cloud resource configuration, comprising: Obtaining cloud resource usage data of each cloud service instance, and preprocessing the cloud resource usage data of each cloud service instance respectively to obtain preprocessed cloud resource usage data of each cloud service instance; The pre-processed cloud resource usage data of all cloud service instances is clustered and analyzed to obtain k clustering results; among them, k is a natural number greater than 1. Any clustering result includes pre-processed cloud resource usage data of one or more cloud service instances, and each clustering result is bound to a different classification label. Perform regression analysis on the pre-processed cloud resource usage data in each clustering result to obtain the resource usage prediction value corresponding to each clustering result; Perform time series forecasting on the resource usage forecast values ​​corresponding to each clustering result to obtain the resource usage forecast values ​​corresponding to each clustering result in a future specified time period; Based on the predicted value of resource usage corresponding to each clustering result within a specified future time period, cloud resource configuration is performed for the cloud service instances in each clustering result.

[0006] In a possible design, cluster analysis is performed on the pre-processed cloud resource usage data of all cloud service instances to obtain k Clustering results include: Obtain the number of clusters of the preprocessed cloud resource usage data k ; initialization k The pre-processed cloud resource usage data of each cloud service instance is assigned to the cluster with the closest Euclidean distance to the cluster center. Calculate the mean of the preprocessed cloud resource usage data of all cloud service instances in the cluster of each cluster center, and use the mean corresponding to each cluster center to update each cluster center to obtain k Updated cluster centers; Set each cluster center to the corresponding updated cluster center, and recalculate the pre-processed cloud resource usage data of each cloud service instance to k The Euclidean distance of the cluster centers is calculated until the mean change of the preprocessed cloud resource usage data of all cloud service instances in the cluster cluster of each cluster center is less than the threshold. k The clusters corresponding to the cluster centers are composed of k Clustering results.

[0007] In one possible design, i Preprocessed cloud resource usage data for each cloud service instance To j Cluster Centers The Euclidean distance is: ; in, Represents the pre-processed cloud resource usage data of a cloud service instance The m data points; M Pre-processed cloud resource usage data for cloud service instances The total number of data points in .

[0008] In a possible design, a regression analysis is performed on the pre-processed cloud resource usage data in any clustering result to obtain a resource usage prediction value corresponding to any clustering result, including: Building an initial regression model and obtaining historical cloud resource usage data that matches a classification label of any clustering result; Based on the historical cloud resource usage data, training the initial regression model to obtain a trained regression model; The pre-processed cloud resource usage data in any of the clustering results is input into the trained regression model to obtain a resource usage prediction value corresponding to any of the clustering results.

[0009] In one possible design, the trained regression model is: ; in, y is the target variable; is the independent variable; is the preset error value; is the regression coefficient.

[0010] In a possible design, a time series prediction process is performed on a resource usage prediction value corresponding to any clustering result to obtain a resource usage prediction value corresponding to any clustering result in a future specified time period, including: Building an initial time series model and obtaining historical cloud resource usage data matching a classification label of any of the clustering results; Based on the historical cloud resource usage data, training the initial time series model to obtain a trained time series model; Input the resource usage prediction value corresponding to any clustering result into the trained time series model to obtain the future specified time period t The resource usage prediction value corresponding to any of the clustering results.

[0011] In one possible design, the trained time series model is: ; in, Specify a time period in the future t A predicted value of resource usage corresponding to any of the clustering results; For the t -1. t -2, … and t - p The predicted value of resource usage corresponding to any clustering result within the time period; is the autoregression coefficient; is the moving average coefficient; p is the autoregressive order; q is the moving average order; For the t -1. t -2, ..., t - q and t Random error in time period.

[0012] In a second aspect, the present invention provides a cost optimization system based on cloud resource configuration, which is used to implement the cost optimization method based on cloud resource configuration as described in any one of the above; the cost optimization system based on cloud resource configuration includes: The initial data acquisition module is used to acquire the cloud resource usage data of each cloud service instance, and pre-process the cloud resource usage data of each cloud service instance to obtain the pre-processed cloud resource usage data of each cloud service instance; The cluster analysis module is connected to the initial data acquisition module and is used to perform cluster analysis on the pre-processed cloud resource usage data of all cloud service instances to obtain k clustering results; among them, k is a natural number greater than 1. Any clustering result includes pre-processed cloud resource usage data of one or more cloud service instances, and each clustering result is bound to a different classification label. A regression analysis module, which is in communication with the cluster analysis module and is used to perform regression analysis on the pre-processed cloud resource usage data in each clustering result to obtain a resource usage prediction value corresponding to each clustering result; A time series prediction module, which is in communication with the regression analysis module and is used to perform time series prediction processing on the resource usage prediction values ​​corresponding to each clustering result to obtain the resource usage prediction values ​​corresponding to each clustering result in a future specified time period; The cloud resource configuration module is in communication with the time series prediction module and is used to configure cloud resources for the cloud service instances in each clustering result based on the predicted value of resource usage corresponding to each clustering result in a future specified time period.

[0013] In a third aspect, the present invention provides an electronic device, comprising: a memory for storing computer program instructions; and, A processor is used to execute the computer program instructions to complete the operation of the cost optimization method based on cloud resource configuration as described in any one of the above.

[0014] In a fourth aspect, the present invention provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements the cost optimization method based on cloud resource configuration as described in any one of the above.

[0015] The beneficial effects of the present invention are: The present invention discloses a cost optimization method, system, device and product based on cloud resource configuration, which can realize intelligent and automated management of cloud resources, optimize cloud resource configuration costs during cloud resource configuration, and ensure the balance between resource utilization and cost. Specifically, in the implementation process of the present invention, first, the cloud resource usage data of each cloud service instance is obtained, and the cloud resource usage data of each cloud service instance is preprocessed to obtain the preprocessed cloud resource usage data of each cloud service instance; then, the preprocessed cloud resource usage data of all cloud service instances is clustered and analyzed to obtain k clustering results; then, the pre-processed cloud resource usage data in each clustering result is respectively subjected to regression analysis to obtain the resource usage prediction value corresponding to each clustering result; then the resource usage prediction value corresponding to each clustering result is subjected to time series prediction processing to obtain the resource usage prediction value corresponding to each clustering result in the future specified time period; finally, based on the resource usage prediction value corresponding to each clustering result in the future specified time period, cloud resource configuration is performed for the cloud service instances in each clustering result. In this process, through clustering analysis processing, cloud resource usage data can be divided into multiple categories, and instances with similar resource usage patterns can be classified into the same category, thereby forming different cloud service instance classification sets, so as to provide more detailed input data for subsequent cloud resource demand forecasting and other analyses, regression analysis processing can generate preliminary resource usage prediction data, and time series prediction processing can generate specific time series prediction results for future time periods, which can effectively deal with the uncertainty of workloads and ensure that the final resource configuration strategy can accurately match the dynamically changing needs, thereby optimizing resource utilization and cost, and ensuring the optimal balance between resource utilization and cost. Based on the present invention, it is possible to improve resource utilization efficiency, reduce operating costs, maximize resource utilization efficiency and minimize costs, and also improve system stability and user experience, thus meeting the complex needs of a modern cloud computing environment.

[0016] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the cost optimization method based on cloud resource configuration in Example 1; Figure 2 is a module block diagram of the cost optimization system based on cloud resource configuration in Example 2; Figure 3 This is a module block diagram of the electronic device in Example 3. DETAILED DESCRIPTION

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0019] Embodiment 1: This embodiment discloses a cost optimization method based on cloud resource configuration, which can be executed by, but is not limited to, a computer device or a virtual machine with certain computing resources, such as a personal computer, a smart phone, a personal digital assistant, or a wearable device, or by a virtual machine.

[0020] like Figure 1 As shown, a cost optimization method based on cloud resource configuration may include but is not limited to the following steps: S1. Obtain the cloud resource usage data of each cloud service instance, and preprocess the cloud resource usage data of each cloud service instance respectively to obtain the preprocessed cloud resource usage data of each cloud service instance; it should be noted that a cloud service instance is an independent computing resource unit or service instance provided to users on a cloud computing platform, which can be a virtual machine instance, a container instance or a database instance, etc., and is usually used to provide an isolated computing environment for different users or different applications, and therefore can be used to distinguish different users or different applications.

[0021] Specifically, in this embodiment, the cloud resource usage data includes usage data, performance indicator data, cost information and other data of the corresponding cloud service instance, among which the usage data is indicator data that directly reflects resource consumption and use, including but not limited to CPU (Central Processing Unit) utilization, memory occupancy, network traffic and disk I / O (Input / Output) volume, etc., which can be used to measure the resource usage of the cloud service instance and is an important reference for cloud resource allocation and optimization in this embodiment. The performance indicator data is various indicators that reflect the performance of the cloud service instance, including but not limited to latency, throughput, response time and error rate, etc., which can be used to further evaluate the operating status and service quality of the system or application.

[0022] In this embodiment, the cloud resource usage data is preprocessed, such as including data cleaning, outlier removal, invalid data screening, standardization, normalization and time series resampling of the cloud resource usage data. Among them, standardization and normalization can be used to eliminate the influence of different data scales. The preprocessing process can ensure the accuracy and effectiveness of subsequent data analysis.

[0023] S2. Perform cluster analysis on the pre-processed cloud resource usage data of all cloud service instances to obtain k clustering results; among them, k is a natural number greater than 1. Any clustering result includes the preprocessed cloud resource usage data of one or more cloud service instances. Any clustering result includes the clustering cluster of the current clustering center. Each clustering result is bound to a different classification label, which is used to identify which clustering result each cloud service instance belongs to.

[0024] Specifically, in step S2, cluster analysis is performed on the pre-processed cloud resource usage data of all cloud service instances to obtain k Clustering results include: S201. Obtaining the number of clusters of the pre-processed cloud resource usage data k ; Specifically, in this embodiment, the appropriate number of clusters for the pre-processed cloud resource usage data of all cloud service instances can be determined by the elbow method or the silhouette coefficient method, which is not limited here.

[0025] S202. Initialization k The pre-processed cloud resource usage data of each cloud service instance is assigned to the cluster with the closest Euclidean distance to the cluster center. In step S202 of this embodiment, i Preprocessed cloud resource usage data for each cloud service instance To j Cluster Centers The Euclidean distance is: ; in, Represents the pre-processed cloud resource usage data of a cloud service instance The m data points; M Pre-processed cloud resource usage data for cloud service instances The total number of data points in ; it should be understood that j ≤k.

[0026] S203. Calculate the mean of the pre-processed cloud resource usage data of all cloud service instances in the cluster of each cluster center, and use the mean corresponding to each cluster center to update each cluster center to obtain k Updated cluster centers; S204. Set each cluster center to the corresponding updated cluster center, and recalculate the pre-processed cloud resource usage data of each cloud service instance to k The Euclidean distance of each cluster center is calculated, that is, returning to step S202 until the mean change of the pre-processed cloud resource usage data of all cloud service instances in the cluster cluster of each cluster center is less than the threshold value, that is, until the cluster center no longer changes significantly. k The clusters corresponding to the cluster centers are composed of k Clustering results.

[0027] S3. Perform regression analysis on the pre-processed cloud resource usage data in each clustering result to obtain a resource usage prediction value corresponding to each clustering result; Specifically, in step S3, a regression analysis is performed on the pre-processed cloud resource usage data in any clustering result to obtain a resource usage prediction value corresponding to any clustering result, including: S301. Construct an initial regression model and obtain historical cloud resource usage data that matches a classification label of any clustering result; S302. Based on the historical cloud resource usage data, the initial regression model is trained to obtain a trained regression model; In step S302 of this embodiment, the trained regression model is: ; in, y is the target variable, which is the resource usage in this embodiment; is the independent variable, which is time in this embodiment; is the preset error value; is the regression coefficient.

[0028] Specifically, in this embodiment, the regression coefficient is solved by the least square method or the gradient descent method, and the solution formula is: ; in, is the independent variable matrix in the historical cloud resource usage data, which is a time matrix in this embodiment; is the target variable vector in the historical cloud resource usage data, which is the resource usage in this embodiment.

[0029] In addition, in this embodiment, during the training of the initial regression model, The fitting effect of the model is evaluated by indicators such as the mean square error and the mean square error, and the trained regression model is obtained when the preset conditions are met.

[0030] S303. Input the pre-processed cloud resource usage data in any clustering result into the trained regression model to obtain a resource usage prediction value corresponding to any clustering result.

[0031] S4. Perform time series forecasting on the resource usage prediction values ​​corresponding to each clustering result to obtain the resource usage prediction values ​​corresponding to each clustering result in the future specified time period; Specifically, in step S4, a time series prediction process is performed on the resource usage prediction value corresponding to any clustering result to obtain a resource usage prediction value corresponding to any clustering result in a future specified time period, including: S401. Build an initial time series model and obtain historical cloud resource usage data that matches the classification label of any clustering result; S402. Based on the historical cloud resource usage data, the initial time series model is trained to obtain a trained time series model; In this embodiment, the trained time series model adopts an ARIMA (Autoregressive Integrated Moving Average) model. Specifically, the trained time series model is: ; in, Specify a time period in the future t A predicted value of resource usage corresponding to any of the clustering results; For the t -1. t -2, … and t - p The predicted value of resource usage corresponding to any clustering result within the time period; is the autoregression coefficient; is the moving average coefficient; p is the autoregressive order; q is the moving average order; For the t -1. t -2, ..., t - q and t Random error in time period.

[0032] Specifically, in this embodiment, the moving average coefficient and the autoregressive coefficient are estimated by maximum likelihood estimation or Bayesian method.

[0033] S403. Input the resource usage prediction value corresponding to any clustering result into the trained time series model to obtain the future specified time period t The resource usage prediction value corresponding to any of the clustering results.

[0034] S5. Based on the predicted value of resource usage corresponding to each clustering result in a future specified time period, cloud resource configuration is performed for the cloud service instances in each clustering result.

[0035] This embodiment can realize intelligent and automated management of cloud resources, and can optimize cloud resource configuration costs during cloud resource configuration to ensure a balance between resource utilization and costs. Specifically, during the implementation of this embodiment, first, the cloud resource usage data of each cloud service instance is obtained, and the cloud resource usage data of each cloud service instance is preprocessed to obtain the preprocessed cloud resource usage data of each cloud service instance; then, cluster analysis is performed on the preprocessed cloud resource usage data of all cloud service instances to obtain k clustering results; then, the pre-processed cloud resource usage data in each clustering result is subjected to regression analysis to obtain the resource usage prediction value corresponding to each clustering result; then the resource usage prediction value corresponding to each clustering result is subjected to time series prediction to obtain the resource usage prediction value corresponding to each clustering result in the future specified time period; finally, based on the resource usage prediction value corresponding to each clustering result in the future specified time period, cloud resource configuration is performed on the cloud service instances in each clustering result. In this process, through clustering analysis, cloud resource usage data can be divided into multiple categories, and instances with similar resource usage patterns can be classified into the same category, thereby forming different cloud service instance classification sets, which can provide more detailed input data for subsequent cloud resource demand forecasting and other analyses. Regression analysis can generate preliminary resource usage prediction data, and time series prediction can generate specific time series prediction results for future time periods, which can effectively deal with the uncertainty of workloads and ensure that the final resource configuration strategy can accurately match the dynamically changing needs, thereby optimizing resource utilization and cost, and ensuring the optimal balance between resource utilization and cost. Based on this embodiment, it is not only possible to improve resource utilization efficiency, reduce operating costs, maximize resource utilization efficiency and minimize costs, but also to improve system stability and user experience, and meet the complex needs of a modern cloud computing environment.

[0036] Embodiment 2: This embodiment discloses a cost optimization system based on cloud resource configuration, which is used to implement the cost optimization method based on cloud resource configuration in Embodiment 1; Figure 2 As shown, the cost optimization system based on cloud resource configuration includes: The initial data acquisition module is used to acquire the cloud resource usage data of each cloud service instance, and pre-process the cloud resource usage data of each cloud service instance to obtain the pre-processed cloud resource usage data of each cloud service instance; The cluster analysis module is connected to the initial data acquisition module and is used to perform cluster analysis on the pre-processed cloud resource usage data of all cloud service instances to obtain k clustering results; among them, k is a natural number greater than 1. Any clustering result includes pre-processed cloud resource usage data of one or more cloud service instances, and each clustering result is bound to a different classification label. A regression analysis module, which is in communication with the cluster analysis module and is used to perform regression analysis on the pre-processed cloud resource usage data in each clustering result to obtain a resource usage prediction value corresponding to each clustering result; A time series prediction module, which is in communication with the regression analysis module and is used to perform time series prediction processing on the resource usage prediction values ​​corresponding to each clustering result to obtain the resource usage prediction values ​​corresponding to each clustering result in a future specified time period; The cloud resource configuration module is in communication with the time series prediction module and is used to configure cloud resources for the cloud service instances in each clustering result based on the predicted value of resource usage corresponding to each clustering result in a future specified time period.

[0037] It should be noted that the working process, working details and technical effects of the cost optimization system based on cloud resource configuration provided in this embodiment 2 can be found in embodiment 1 and will not be repeated here.

[0038] Embodiment 3: Based on Embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a laptop computer, or a desktop computer. The electronic device may be referred to as a user terminal, a portable terminal, a desktop terminal, etc. Figure 3 As shown, the electronic equipment includes: a memory for storing computer program instructions; and, A processor is used to execute the computer program instructions to complete the operation of the cost optimization method based on cloud resource configuration as described in any one of Example 1.

[0039] Specifically, the processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen.

[0040] The memory 302 may include one or more computer-readable storage media, which may be non-transitory. The memory 302 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 302 is used to store at least one instruction, which is used to be executed by the processor 301 to implement the cost optimization method based on cloud resource configuration provided in Example 1 of the present application.

[0041] In some embodiments, the terminal may further optionally include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected via a bus or a signal line. Each peripheral device may be connected to the communication interface 303 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 304, a display screen 305 and a power supply 306.

[0042] The communication interface 303 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0043] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices through electromagnetic signals.

[0044] The display screen 305 is used to display a UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof.

[0045] The power supply 306 is used to supply power to various components in the electronic device.

[0046] Embodiment 4: Based on any one of Examples 1 to 3, this embodiment discloses a computer program product, including a computer program or instructions, which, when executed by a computer, implements the cost optimization method based on cloud resource configuration as described in any one of Example 1.

[0047] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cost optimization method based on cloud resource configuration, characterized in that: include: Obtaining cloud resource usage data of each cloud service instance, and preprocessing the cloud resource usage data of each cloud service instance respectively to obtain preprocessed cloud resource usage data of each cloud service instance; The pre-processed cloud resource usage data of all cloud service instances is clustered and analyzed to obtain k clustering results; among them, k is a natural number greater than 1. Any clustering result includes pre-processed cloud resource usage data of one or more cloud service instances, and each clustering result is bound to a different classification label. Perform regression analysis on the pre-processed cloud resource usage data in each clustering result to obtain the resource usage prediction value corresponding to each clustering result; Performing time series forecasting processing on the resource usage forecast values ​​corresponding to each clustering result to obtain the resource usage forecast values ​​corresponding to each clustering result in a future specified time period; Based on the predicted value of resource usage corresponding to each clustering result within a specified future time period, cloud resource configuration is performed for the cloud service instances in each clustering result.

2. A cost optimization method based on cloud resource configuration according to claim 1, characterized in that: The pre-processed cloud resource usage data of all cloud service instances is clustered and analyzed to obtain k Clustering results include: Obtain the number of clusters of the preprocessed cloud resource usage data k ; initialization k The pre-processed cloud resource usage data of each cloud service instance is assigned to the cluster with the closest Euclidean distance to the cluster center. Calculate the mean of the preprocessed cloud resource usage data of all cloud service instances in the cluster of each cluster center, and use the mean corresponding to each cluster center to update each cluster center to obtain k Updated cluster centers; Set each cluster center to the corresponding updated cluster center, and recalculate the pre-processed cloud resource usage data of each cloud service instance to k The Euclidean distance of the cluster centers is calculated until the mean change of the preprocessed cloud resource usage data of all cloud service instances in the cluster cluster of each cluster center is less than the threshold. k The clusters corresponding to the cluster centers are composed of k Clustering results.

3. The cost optimization method based on cloud resource configuration according to claim 1, characterized in that: No. i Preprocessed cloud resource usage data for each cloud service instance To j Cluster Centers The Euclidean distance is: ； in, Represents the pre-processed cloud resource usage data of a cloud service instance The m data points; M Pre-processed cloud resource usage data for cloud service instances The total number of data points in .

4. The cost optimization method based on cloud resource configuration according to claim 1, characterized in that: Performing regression analysis on the pre-processed cloud resource usage data in any clustering result to obtain a resource usage prediction value corresponding to any clustering result, including: Building an initial regression model and obtaining historical cloud resource usage data that matches a classification label of any clustering result; Based on the historical cloud resource usage data, training the initial regression model to obtain a trained regression model; The pre-processed cloud resource usage data in any of the clustering results is input into the trained regression model to obtain a resource usage prediction value corresponding to any of the clustering results.

5. The cost optimization method based on cloud resource configuration according to claim 4, characterized in that: The regression model after training is: ； in, y is the target variable; is the independent variable; is the preset error value; is the regression coefficient.

6. The cost optimization method based on cloud resource configuration according to claim 1, characterized in that: Performing time series prediction processing on the resource usage prediction value corresponding to any clustering result to obtain the resource usage prediction value corresponding to any clustering result in a future specified time period, including: Building an initial time series model and obtaining historical cloud resource usage data matching a classification label of any of the clustering results; Based on the historical cloud resource usage data, training the initial time series model to obtain a trained time series model; Input the resource usage prediction value corresponding to any clustering result into the trained time series model to obtain the future specified time period t The resource usage prediction value corresponding to any of the clustering results.

7. The cost optimization method based on cloud resource configuration according to claim 6, characterized in that: The trained time series model is: ； in, Specify a time period in the future t A predicted value of resource usage corresponding to any of the clustering results; For the t -1. t -2, … and t - p The predicted value of resource usage corresponding to any clustering result within the time period; is the autoregression coefficient; is the moving average coefficient; p is the autoregressive order; q is the moving average order; For the t -1. t -2, ..., t - q and t Random error in time period.

8. A cost optimization system based on cloud resource configuration, characterized in that: Used to implement the cost optimization method based on cloud resource configuration as described in any one of claims 1 to 7; The cost optimization system based on cloud resource configuration includes: The initial data acquisition module is used to acquire the cloud resource usage data of each cloud service instance, and pre-process the cloud resource usage data of each cloud service instance to obtain the pre-processed cloud resource usage data of each cloud service instance; The cluster analysis module is connected to the initial data acquisition module and is used to perform cluster analysis on the pre-processed cloud resource usage data of all cloud service instances to obtain k clustering results; among them, k is a natural number greater than 1. Any clustering result includes pre-processed cloud resource usage data of one or more cloud service instances, and each clustering result is bound to a different classification label. A regression analysis module, which is in communication with the cluster analysis module and is used to perform regression analysis on the pre-processed cloud resource usage data in each clustering result to obtain a resource usage prediction value corresponding to each clustering result; A time series prediction module, which is in communication with the regression analysis module and is used to perform time series prediction processing on the resource usage prediction values ​​corresponding to each clustering result to obtain the resource usage prediction values ​​corresponding to each clustering result in a future specified time period; The cloud resource configuration module is in communication with the time series prediction module and is used to configure cloud resources for the cloud service instances in each clustering result based on the predicted value of resource usage corresponding to each clustering result in a future specified time period.

9. An electronic device, characterized in that: include: a memory for storing computer program instructions; as well as, A processor is used to execute the computer program instructions to complete the operation of the cost optimization method based on cloud resource configuration as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or the instruction is executed by a computer, the cost optimization method based on cloud resource configuration as described in any one of claims 1 to 7 is implemented.

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