Method and system for analyzing and evaluating resource utilization rate of state grid cloud platform

Abstract

The invention discloses a state grid cloud platform resource utilization rate analysis and evaluation method and system. The method comprises the steps of collecting first service system data and a first operation performance index; analyzing the first operation performance index through a first index quantile algorithm; constructing a state grid cloud resource utilization rate analysis and evaluation model and a first utilization rate scoring rule for the first operation performance index to obtain cloud resource utilization rate detail data; and constructing a resource utilization rate data warehouse by using the first service system data and the detailed data so as to perform multi-dimensional analysis on the cloud resource utilization rate. Based on the performance index data generated in the operation of various resource instances of the cloud platform, the resource utilization rate of the operation of the cloud platform is analyzed in real time, the resource use conditions of each unit, each information system and various resources are grasped in time, zombie resources are found in time, and the resource utilization rate is gradually improved. Compared with an existing method, the resource utilization rate analysis is more accurate, and the actual cloud platform operation service requirements are better met.

Description

Technical Field

[0001] The present invention relates to the technical field of resource operation analysis, and in particular to a method and system for analyzing and evaluating resource utilization of a State Grid cloud platform. Background Art

[0002] The State Grid Corporation of China has built its cloud platform based on Alibaba Cloud and Huawei Cloud, in accordance with the architecture of one cloud and multiple regions. Currently, Alibaba Cloud and Huawei Cloud provide their own cloud platform management tools, which can manage the creation, expansion, reduction, release, start and stop of their own cloud platform resources. At the same time, they provide some simple resource analysis and statistical functions. The analysis indicators and analysis dimensions are not unified, and there is a lack of multi-dimensional aggregation analysis and drill-down analysis, as well as lack of analysis that fits the business level. As a result, cloud platform operators cannot grasp the utilization of cloud platform resources and cannot support related work of cloud resource operation. At present, the analysis of cloud platform resource utilization mainly has the following difficulties:

[0003] ① The cloud platform resource utilization analysis algorithm is simple: The current cloud platform resource utilization analysis algorithm adopts traditional algorithms such as averaging and maximum value. Such algorithms are too simple and cannot reflect the actual operating efficiency of resources, cannot cover business scenarios with peaks and valleys, and the resource utilization analysis is inaccurate. ② There are many types of cloud platform resources, and the operating indicators are scattered and unsystematic: There are many types of cloud platform resources, including cloud hosts, cloud databases, cloud containers, cloud networks, and cloud storage resources. Each type of resource has different operating performance indicators. There is a lack of overall planning and design of operating performance indicators, and a lack of indicator classification and layered design system, which makes it impossible to support the analysis and calculation of resource utilization analysis models, affecting the integrity and accuracy of resource utilization analysis. ③ The cloud platform resource utilization analysis model is simple: The current cloud platform resource utilization analysis method is mainly based on the analysis of each independent resource instance. There is a lack of a model for comprehensive analysis and evaluation of resource utilization through multiple indicators, and there is a lack of correlation analysis between unit dimensions, information system dimensions, cloud platform dimensions, and time dimensions. It is impossible to quickly aggregate and drill down between multiple dimensions. The resource utilization analysis is not in-depth enough and cannot accurately analyze and evaluate resource usage. ④ The number of cloud platform resource performance indicators is huge, and the analysis and calculation efficiency is low: Due to the large number of cloud platform resource instances, in order to ensure the accuracy of resource utilization analysis, it is necessary to collect performance indicator data of each resource instance on a regular basis 7*24 hours a day, and the amount of data is huge; in addition, the resource utilization analysis process requires multiple dimensions to analyze, including unit dimension, information system dimension, and cloud platform dimension, resulting in a huge amount of analysis and calculation, a long analysis cycle, and low efficiency, which affects the accuracy and completeness of resource utilization analysis. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the purpose of the present invention is to overcome the shortcomings of traditional resource utilization analysis methods, provide a cloud platform resource utilization intelligent analysis method, construct a cloud platform resource utilization analysis model, deeply mine the value information of cloud resource operation indicator data, and provide a set of truly feasible data analysis solutions for cloud platform zombie resource positioning, cloud resource capacity prediction, and cloud resource utilization efficiency analysis and evaluation, so as to achieve operational goals such as cloud platform zombie resource screening, cloud resource drying of each unit, and information system resource utilization evaluation, continuously optimize the utilization of cloud platform resources, improve cloud platform utilization efficiency, and reduce resource waste.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for analyzing and evaluating the resource utilization of a State Grid cloud platform, comprising: collecting first business system data and a first operating performance indicator; analyzing the first operating performance indicator through a first indicator quantile algorithm; constructing a State Grid cloud resource utilization analysis and evaluation model and a first utilization scoring rule for the first operating performance indicator to obtain cloud resource utilization detailed data; and constructing a resource utilization data warehouse using the first business system data and the cloud resource utilization detailed data to perform a multi-dimensional analysis of the cloud resource utilization.

[0008] As a preferred solution of the method for analyzing and evaluating the resource utilization of the State Grid cloud platform of the present invention, wherein: the collection of the first business system data and the first operating performance indicator includes:

[0009] Configure a collection task, which includes a task name, a collection period, and a collection indicator;

[0010] Connecting to the cloud platform and calling the cloud platform interface to obtain the first business system data and the first operation performance indicator;

[0011] The collected data is saved, that is, the first business system data is stored in the State Grid cloud platform configuration management database, and the first operating performance indicator is stored in the time series library and the history library.

[0012] As a preferred solution of the method for analyzing and evaluating the utilization rate of the State Grid cloud platform resources of the present invention, the construction of the State Grid cloud resource utilization analysis and evaluation model includes:

[0013] According to the business requirements of the State Grid cloud platform operation, design a first utilization scoring rule for the first operating performance indicator;

[0014] Constructing a State Grid cloud resource utilization analysis and evaluation model based on the first utilization scoring rule for the first operating performance indicator;

[0015] The State Grid cloud resource utilization analysis and evaluation model is trained through the historical data of cloud resource CPU utilization, memory utilization, disk IO utilization and network bandwidth utilization stored in the historical library, and the parameters of the model are continuously optimized.

[0016] As a preferred solution of the method for analyzing and evaluating the utilization rate of the State Grid cloud platform resources of the present invention, the training of the State Grid cloud resource utilization analysis and evaluation model includes:

[0017] Annotate cloud platform resource data, wherein the annotated information includes resource classification, importance, and time granularity of resource operation indicator data;

[0018] Input the historical data and annotation information of cloud resource CPU utilization, memory utilization, disk IO utilization and network bandwidth utilization stored in the historical database into the State Grid cloud resource utilization analysis and evaluation model for analysis and calculation to obtain cloud platform resource utilization data;

[0019] Evaluate the results of model training, that is, evaluate whether the parameters and weights of the State Grid cloud resource utilization analysis and evaluation model meet the scoring accuracy requirements, and provide optimization suggestions;

[0020] Iterative training is performed according to the optimization suggestions until the accuracy of the training results of the State Grid cloud resource utilization analysis and evaluation model reaches a first threshold, thereby completing the training of the model.

[0021] As a preferred solution of the State Grid cloud platform resource utilization analysis and evaluation method described in the present invention, the trained State Grid cloud resource utilization analysis and evaluation model is used to score the cloud resource CPU utilization, memory utilization, disk IO utilization and network bandwidth utilization respectively, and the sum of the CPU, memory, disk and network bandwidth scores is taken as the total resource scoring result.

[0022] As a preferred solution of the method for analyzing and evaluating the resource utilization of the State Grid cloud platform of the present invention, the analyzing the first operating performance indicator by the first indicator quantile algorithm includes:

[0023] Arranging the first operating performance indicators in ascending order;

[0024] Determine the total amount of data involved in the calculation and the specific quantile to be calculated, and calculate the position of the target quantile in the data set;

[0025] If the calculated target quantile in the data set happens to fall on a certain data point, the value of the data point is the required quantile value; if the calculated target quantile in the data set is between two data points, the interpolation method is used to estimate the value of the target quantile.

[0026] As a preferred solution of the method for analyzing and evaluating the resource utilization of the State Grid cloud platform of the present invention, the construction of the resource utilization data warehouse includes:

[0027] Design resource utilization analysis dimensions, and design resource utilization analysis data drill-down and aggregation rules based on the business requirements of cloud resource operation analysis;

[0028] Based on the resource utilization analysis dimension, resource utilization analysis data drill-down and aggregation rules are designed, and a resource utilization data warehouse is constructed using the first business system data and the cloud resource utilization detail data to perform multi-dimensional analysis of the cloud resource utilization.

[0029] In a second aspect, the present invention provides a State Grid cloud platform resource utilization analysis and evaluation system, comprising:

[0030] A data collection module, used to collect first business system data and first operation performance indicators;

[0031] A data analysis module, configured to analyze the first operating performance indicator by using a first indicator quantile algorithm;

[0032] An analysis and evaluation module, used to construct a State Grid cloud resource utilization analysis and evaluation model and a first utilization scoring rule for the first operating performance indicator to obtain detailed cloud resource utilization data;

[0033] The data warehouse construction module is used to construct a resource utilization data warehouse using the first business system data and the cloud resource utilization detail data to perform multi-dimensional analysis on the cloud resource utilization.

[0034] In a third aspect, the present invention provides a computing device, comprising:

[0035] Memory and processor;

[0036] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the State Grid cloud platform resource utilization analysis and evaluation method are implemented.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the State Grid cloud platform resource utilization analysis and evaluation method.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a method and system for analyzing and evaluating the resource utilization of the State Grid cloud platform, which provides a set of truly feasible data analysis solutions for the resource utilization operation analysis of the State Grid cloud platform, and designs a State Grid cloud platform resource utilization analysis system based on the solution. Based on various performance indicator data generated during the operation of various resource instances of the cloud platform, including CPU utilization, memory utilization, disk IO utilization, and network bandwidth utilization, the resource utilization of the cloud platform operation is analyzed in real time, the resource usage of each unit, each information system, and various resources is timely grasped, zombie resources are discovered in a timely manner, and the utilization of cloud resources is gradually improved. Compared with the existing analysis methods, the resource utilization analysis of the present invention is more accurate and more in line with the actual business requirements of cloud platform operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0040] Figure 1 A schematic diagram of the overall process logic of the method for analyzing and evaluating the utilization rate of State Grid cloud platform resources according to an embodiment of the present invention;

[0041] Figure 2 A schematic diagram of the existing State Grid cloud platform architecture of the State Grid cloud platform resource utilization analysis and evaluation method according to an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of a State Grid cloud platform resource utilization analysis process of a State Grid cloud platform resource utilization analysis and evaluation method according to an embodiment of the present invention;

[0043] Figure 4 This is a diagram of the State Grid cloud platform resource utilization analysis system architecture of the State Grid cloud platform resource utilization analysis and evaluation method described in one embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0045] Example 1

[0046] Reference Figure 1-Figure 4 As an embodiment of the present invention, a method for analyzing and evaluating the resource utilization of a State Grid cloud platform is provided. Figure 1 The specific steps shown include:

[0047] S100: Collecting first business system data and first operation performance indicators;

[0048] S200: Analyzing a first operating performance indicator by using a first indicator quantile algorithm;

[0049] S300: constructing a State Grid cloud resource utilization analysis and evaluation model and a first utilization scoring rule for a first operation performance indicator to obtain detailed cloud resource utilization data;

[0050] S400: construct a resource utilization data warehouse using the first business system data and the cloud resource utilization detail data to perform multi-dimensional analysis on the cloud resource utilization.

[0051] It should be noted that the present invention is based on various performance indicator data generated during the operation of various resource instances on the cloud platform, including CPU utilization, memory utilization, disk IO utilization, and network bandwidth utilization, and analyzes the resource utilization of the cloud platform in real time, timely grasps the resource usage of various units, information systems, and various resources, and timely discovers zombie resources, gradually improving the utilization of cloud resources. Compared with existing analysis methods, the resource utilization analysis of the present invention is more accurate and more in line with the actual business requirements of cloud platform operations.

[0052] In the embodiment of the present application, the above step S100 includes the following sub-steps A1-A3;

[0053] In A1: configure the collection task, which includes the task name, collection cycle, and collection indicators;

[0054] In A2: connecting to the cloud platform and calling the cloud platform interface to obtain the first business system data and the first operation performance indicator;

[0055] In A3: the collected data is saved, that is, the first business system data is stored in the State Grid cloud platform configuration management database, and the first operation performance index is stored in the time series library and the history library.

[0056] In an optional embodiment, the first business system data may be user behavior related data, service request related data, content generation data, or system log data;

[0057] Specifically, in this embodiment, the first business system data is data collected from the business system cloud application work order, including the business system name, business system code, affiliated unit, cloud mode, cloud time, affiliated resource set, affiliated organization, deployment mode and affiliated business domain information, as detailed in Figure 2 shown.

[0058] In an optional embodiment, the first operating performance indicator may be CPU utilization, memory usage, disk I / O, response time, number of concurrent connections, network throughput, error rate, or availability;

[0059] Specifically, in this embodiment, the first operating performance indicator is the average CPU utilization, CPU peak utilization, average memory utilization, memory peak utilization, disk IO average utilization, disk IO peak utilization, network bandwidth average utilization and network bandwidth maximum utilization of all resource instances collected at a 5-minute granularity, where all resource instances are cloud hosts, cloud databases, container resources, cloud networks and cloud storage of the entire network cloud platform. The data includes resource instance ID, resource instance name, resource set, organization and resource instance specification information, as detailed in Figure 2 shown.

[0060] Specifically, the first running performance indicators after collection are stored and divided into a history library and a time series library, such as Figure 3 The historical database shown uses big data HBase to achieve massive data storage, and the time series database uses InfluxDB for storage. In addition, a cloud platform CMDB library is built to store the relationship data between cloud business systems, organizations, projects and cloud resources. The CMDB library uses a relational database for storage.

[0061] It should be noted that the above step S100 ensures the accuracy and consistency of data collection by configuring the collection task, avoiding data deviation caused by unclear tasks. Connecting to the cloud platform and calling the interface to automatically obtain data not only improves the real-time and accuracy of data collection, but also greatly reduces the time and cost of manual operation. In addition, different types of collected data are stored in appropriate databases, such as storing the first business system data in the State Grid cloud platform configuration management database, and the first operating performance indicators are stored in the time series library and the historical library. This hierarchical storage method not only optimizes data management, but also facilitates subsequent data query and analysis, and improves the efficiency of data utilization.

[0062] In an optional embodiment, the first indicator quantile algorithm may adopt a percentile algorithm to determine the position of a value in a data set. For example, by calculating the 95th percentile of CPU utilization, the CPU usage during most of the time may be understood; the first indicator quantile algorithm may adopt a quartile algorithm, that is, the first quartile indicates that 25% of the data is lower than or equal to the value, the second quartile is the median, indicating that 50% of the data is lower than or equal to the value, and the third quartile indicates that 75% of the data is lower than or equal to the value; the first indicator quantile algorithm may also adopt a quantile regression algorithm, that is, estimating the conditional distribution of the dependent variable at different quantiles;

[0063] In the embodiment of the present application, the first indicator quantile algorithm adopts a percentile algorithm, and the process of analyzing the first operating performance indicator by the percentile algorithm includes:

[0064] Arranging the first operating performance index in ascending order;

[0065] Determine the total amount of data involved in the calculation and the specific quantile to be calculated, and calculate the position of the target quantile in the data set;

[0066] If the calculated target quantile in the data set happens to fall on a certain data point, the value of the data point is the required quantile value; if the calculated target quantile in the data set is between two data points, the interpolation method is used to estimate the value of the target quantile;

[0067] Specifically, the calculation of the position of the target quantile in the data set is expressed as:

[0068] position=1+(n-1)*p

[0069] Where n represents the number of indicator data involved in the calculation, and p represents the percentile, such as 0.95 (95th percentile), 0.90 (90th percentile), and 0.75 (75th percentile);

[0070] Specifically, the specific value at the p quantile position is calculated as follows:

[0071]

[0072] Among them, position(p) represents the position of a specific quantile, data[p] represents the data of a specific quantile, and figure(p) represents the quantile of the p quantile.

[0073] It should be noted that the above step S200 analyzes the first operating performance indicator through the percentile algorithm, which significantly improves the accuracy and reliability of performance evaluation, can effectively eliminate the influence of extreme values, provide more stable and representative performance evaluation results, and also provides reliable data support for subsequent resource utilization analysis and optimization.

[0074] In the embodiment of the present application, the above step S300 includes the following sub-steps C1-C3;

[0075] In C1: Build a State Grid cloud resource utilization analysis and evaluation model and train the model;

[0076] In C2: constructing a first utilization scoring rule for the first operation performance indicator;

[0077] In C3: Based on the first utilization scoring rule, the performance indicators are calculated through the trained State Grid cloud resource utilization analysis and evaluation model to obtain detailed cloud resource utilization data.

[0078] Specifically, constructing the State Grid cloud resource utilization analysis and evaluation model includes: designing a first utilization scoring rule for a first operating performance indicator according to the business requirements of the State Grid cloud platform operation; constructing the State Grid cloud resource utilization analysis and evaluation model based on the first utilization scoring rule for the first operating performance indicator; training the State Grid cloud resource utilization analysis and evaluation model through historical data of cloud resource CPU utilization, memory utilization, disk IO utilization, and network bandwidth utilization stored in a historical library, and continuously optimizing the parameters of the model;

[0079] Specifically, the training of the State Grid cloud resource utilization analysis and evaluation model includes: labeling the cloud platform resource data, the labeled information includes the resource classification, importance and time granularity of the resource operation indicator data; inputting the historical data of cloud resource CPU utilization, memory utilization, disk IO utilization and network bandwidth utilization stored in the historical library and the labeled information into the State Grid cloud resource utilization analysis and evaluation model for analysis and calculation to obtain the cloud platform resource utilization data; evaluating the results of the model training, that is, evaluating whether the parameters and weights of the State Grid cloud resource utilization analysis and evaluation model meet the scoring accuracy requirements, and giving optimization suggestions; iterative training is performed according to the optimization suggestions until the accuracy of the training results of the State Grid cloud resource utilization analysis and evaluation model reaches the first threshold, and the training of the model is completed.

[0080] In an optional embodiment, the first threshold is 90%, wherein the 90% accuracy standard prompts the model to be continuously optimized and iterated during the training process, ensuring the robustness and generalization ability of the model, and being able to maintain stable performance in different scenarios and conditions.

[0081] Specifically, we use big data batch computing technology, access cloud resource batch operation performance indicator data, and use the trained State Grid cloud resource utilization analysis and evaluation model to score the cloud resource CPU utilization, memory utilization, disk IO utilization, and network bandwidth utilization respectively, and take the sum of the CPU, memory, disk, and network bandwidth scores as the total resource scoring result, such as Figure 4 shown.

[0082] In an optional embodiment, the first utilization scoring rule is an important mechanism for evaluating and quantifying cloud resource utilization, and different scoring criteria can be set for each type of resource according to the actual business requirements of the State Grid cloud platform operation;

[0083] Exemplarily, the first utilization scoring rule of this embodiment is shown in Table 1, which scores CPU utilization, memory utilization, disk IO utilization, and network bandwidth utilization respectively, and takes the sum of the scores of CPU, memory, disk, and network as the resource scoring result, with a full score of 100 points;

[0084] Table 1: Specific parameter scoring table.

[0085]

[0086]

[0087] ①CPU utilization: per unit time, (cloud resource CPU non-idle running time / cloud resource CPU total time)*100, reflecting the CPU busyness;

[0088] ②Memory utilization: per unit time, (the amount of memory used by cloud resources / the total amount of memory of cloud resources)*100, which reflects the consumption of memory resources;

[0089] ③Disk IO utilization: per unit time, (cloud resource disk read and write data volume / cloud resource disk read and write data volume upper limit)*100, reflecting the cloud resource disk read and write busyness;

[0090] ④ Network bandwidth utilization: per unit time, (cloud resource inflow and outflow network traffic / cloud resource inflow and outflow network traffic upper limit)*100, which reflects the busyness of cloud resource network data transmission.

[0091] It should be noted that the above step S300 not only ensures the accuracy and reliability of the model, but also provides comprehensive resource utilization detailed data through scientific scoring rules and training optimization. Specifically, building and training models makes the evaluation of resource utilization more intelligent and automated, and can adapt to complex changes in cloud environments. The introduction of the first utilization scoring rule ensures the accuracy and representativeness of the evaluation results through the quantile algorithm and multi-dimensional scoring criteria. Finally, the performance indicators are calculated through the trained model, and the cloud resource utilization detailed data obtained can not only reflect the actual use of resources, but also provide a solid data foundation for subsequent multi-dimensional analysis and optimization, significantly improving the efficiency and effectiveness of resource management.

[0092] In the embodiments of the present application, Figure 3 The above step S400 uses the first business system data and the cloud resource utilization detail data to construct a resource utilization data warehouse, including:

[0093] Design resource utilization analysis dimensions, and design resource utilization analysis data drill-down and aggregation rules based on the business requirements of cloud resource operation analysis;

[0094] Based on the resource utilization analysis dimension, design resource utilization analysis data drill-down and aggregation rules, use the first business system data and cloud resource utilization detailed data to build a resource utilization data warehouse to conduct multi-dimensional analysis of cloud resource utilization, and have the function of supporting multi-dimensional flexible query and analysis of upper-level applications.

[0095] It should be noted that the above step S400 not only ensures the comprehensiveness and accuracy of the data, but also supports the flexible query requirements of upper-level applications through multi-dimensional analysis functions, making the analysis of resource utilization more in-depth and detailed, and being able to explore data from multiple angles and levels, thereby helping managers to understand resource usage more comprehensively and discover potential problems and optimization points. At the same time, the construction of the data warehouse provides a strong data foundation for various report generation and decision support, making business decisions more scientific and efficient.

[0096] The above is a schematic scheme of a method for analyzing and evaluating the utilization rate of resources of a State Grid cloud platform in this embodiment. It should be noted that the technical scheme of the system for analyzing and evaluating the utilization rate of resources of the State Grid cloud platform and the technical scheme of the method for analyzing and evaluating the utilization rate of resources of the State Grid cloud platform belong to the same concept. For details not described in detail in the technical scheme of the system for analyzing and evaluating the utilization rate of resources of the State Grid cloud platform in this embodiment, please refer to the description of the technical scheme of the method for analyzing and evaluating the utilization rate of resources of the State Grid cloud platform.

[0097] This embodiment also provides a State Grid cloud platform resource utilization analysis and evaluation system, including:

[0098] A data collection module, used to collect first business system data and first operation performance indicators;

[0099] A data analysis module, used for analyzing the first operating performance indicator by using a first indicator quantile algorithm;

[0100] An analysis and evaluation module, used to construct a State Grid cloud resource utilization analysis and evaluation model and a first utilization scoring rule for a first operating performance indicator to obtain detailed cloud resource utilization data;

[0101] The data warehouse construction module is used to construct a resource utilization data warehouse using the first business system data and the cloud resource utilization detail data to perform multi-dimensional analysis on the cloud resource utilization.

[0102] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0103] This embodiment also provides a computing device, which includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for analyzing and evaluating the resource utilization of a State Grid cloud platform is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0104] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiment is implemented.

[0105] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0106] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0107] Example 2

[0108] Based on the previous embodiment, this embodiment provides an application example of a State Grid cloud platform resource utilization analysis and evaluation method and system to verify and illustrate the technical effects used in this method.

[0109] This embodiment takes the ECS resource utilization analysis of the Alibaba Cloud platform of a provincial company of State Grid as an example. The CPU utilization, memory utilization, disk IO utilization, and network bandwidth utilization indicators of all ECS instance resources are collected at a granularity of 5 minutes. The collection is continued for one month (2023.3.1 to 2023.3.30), abnormal data values ​​are eliminated, and the collected results are stored in the history library as sample data.

[0110] Based on the CPU utilization, memory utilization, disk IO utilization and network bandwidth utilization data in the historical database, the utilization of each ECS resource is sorted in ascending order, and the quantile algorithm is used to iteratively calculate the utilization of each ECS resource. Users judge and feedback the calculation results based on the actual operation of the system. The system continuously adjusts and optimizes the quantile calculation results based on user feedback, and finally determines an accurate and reasonable quantile. Table 2 shows the values ​​of the various utilization indicators of ECS resources calculated by the system at the 95th, 90th, 85th, 80th and 75th percentiles, and finally determines that the various utilization indicators of ECS calculated at the 80th percentile are reasonable and accurate (in actual conditions, the specific determination of which quantile value is the most accurate needs to be finally determined based on different sample data and training results).

[0111] Table 2: Sample data quantile calculation training

[0112] Utilization Percentile 95th percentile 90th percentile 85th percentile 80th percentile 75th percentile CPU Utilization 100.00％ 85.38％ 58.98％ 46.78％ 32.18％ Memory Utilization 97.54％ 78.71％ 62.64％ 50.35％ 34.76％ Disk IO utilization 84.39％ 66.91％ 50.27％ 42.87％ 28.18％ Network bandwidth utilization 69.24％ 57.58％ 42.86％ 38.66％ 23.36％

[0113] According to the training results, the 80th percentile is the most reasonable and accurate way to calculate the resource utilization of ECS. Based on this result, the real-time collected ECS resource utilization is calculated at the 80th percentile and compared with the traditional average and median values. The results are shown in Table 3.

[0114] Table 3: Comparison of real-time calculation of ECS resource utilization.

[0115]

[0116]

[0117] Compared with the traditional average and median algorithms, the traditional average algorithm is affected by the low business volume at night, resulting in too low resource utilization in calculations; the median algorithm has a certain degree of optimization in calculation results compared to the average algorithm, but it still cannot reflect the impact of the daytime business peak period, and the calculation results still cannot truly reflect the actual business situation; the 80th percentile algorithm, which has undergone iterative training, analyzes and calculates the utilization of various ECS resources, which is more in line with the actual situation of actual business operations, and the evaluation of resource utilization is more objective and reasonable.

[0118] Finally, based on the calculation results of the 80th percentile, the ECS resource utilization is scored and evaluated according to the evaluation model in Table 1, as shown in Table 4.

[0119] Table 4: ECS resource utilization scores.

[0120]

[0121]

[0122] Therefore, it can be seen from the above that this embodiment provides a cloud platform resource utilization intelligent analysis method, constructs a cloud platform resource utilization analysis model, deeply mines the value information of cloud resource operation indicator data, and provides a set of truly feasible data analysis solutions for cloud platform zombie resource positioning, cloud resource capacity prediction, and cloud resource utilization efficiency analysis and evaluation, etc., to achieve operational goals such as cloud platform zombie resource screening, cloud resource drying of each unit, and information system resource utilization rate evaluation, continuously optimize the utilization of cloud platform resources, improve cloud platform utilization efficiency, and reduce resource waste.

[0123] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0124] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0125] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0126] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0127] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0128] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0129] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for analyzing and evaluating the resource utilization of a State Grid cloud platform, characterized in that: include: Collecting first business system data and first operation performance indicators; Analyzing the first operating performance indicator by a first indicator quantile algorithm; Constructing a State Grid cloud resource utilization analysis and evaluation model and a first utilization scoring rule for the first operating performance indicator to obtain detailed cloud resource utilization data; A resource utilization data warehouse is constructed using the first business system data and the cloud resource utilization detail data to perform multi-dimensional analysis on the cloud resource utilization.

2. The method for analyzing and evaluating the resource utilization of the State Grid cloud platform according to claim 1, characterized in that: The collecting of the first business system data and the first operation performance indicator comprises: Configure a collection task, which includes a task name, a collection period, and a collection indicator; Connecting to the cloud platform and calling the cloud platform interface to obtain the first business system data and the first operation performance indicator; The collected data is saved, that is, the first business system data is stored in the State Grid cloud platform configuration management database, and the first operating performance indicator is stored in the time series library and the history library.

3. The method for analyzing and evaluating the resource utilization of the State Grid cloud platform according to claim 2, characterized in that: The construction of the State Grid cloud resource utilization analysis and evaluation model includes: According to the business requirements of the State Grid cloud platform operation, design a first utilization scoring rule for the first operating performance indicator; Constructing a State Grid cloud resource utilization analysis and evaluation model based on the first utilization scoring rule for the first operating performance indicator; The State Grid cloud resource utilization analysis and evaluation model is trained through the historical data of cloud resource CPU utilization, memory utilization, disk IO utilization and network bandwidth utilization stored in the historical library, and the parameters of the model are continuously optimized.

4. The method for analyzing and evaluating the resource utilization of the State Grid cloud platform according to claim 3, characterized in that: Training the State Grid cloud resource utilization analysis and evaluation model includes: Annotate cloud platform resource data, wherein the annotated information includes resource classification, importance, and time granularity of resource operation indicator data; Input the historical data and annotation information of cloud resource CPU utilization, memory utilization, disk IO utilization and network bandwidth utilization stored in the historical database into the State Grid cloud resource utilization analysis and evaluation model for analysis and calculation to obtain cloud platform resource utilization data; Evaluate the results of model training, that is, evaluate whether the parameters and weights of the State Grid cloud resource utilization analysis and evaluation model meet the scoring accuracy requirements, and provide optimization suggestions; Iterative training is performed according to the optimization suggestions until the accuracy of the training results of the State Grid cloud resource utilization analysis and evaluation model reaches a first threshold, thereby completing the training of the model.

5. The method for analyzing and evaluating the resource utilization of the State Grid cloud platform according to claim 4, characterized in that: The trained State Grid cloud resource utilization analysis and evaluation model is used to score the cloud resource CPU utilization, memory utilization, disk IO utilization and network bandwidth utilization respectively, and the sum of the CPU, memory, disk and network bandwidth scores is taken as the total resource scoring result.

6. The method for analyzing and evaluating the resource utilization of the State Grid cloud platform according to claim 1, characterized in that: Analyzing the first operating performance indicator by using a first indicator quantile algorithm includes: Arranging the first operating performance indicators in ascending order; Determine the total amount of data involved in the calculation and the specific quantile to be calculated, and calculate the position of the target quantile in the data set; If the calculated target quantile in the data set happens to fall on a certain data point, the value of the data point is the required quantile value; if the calculated target quantile in the data set is between two data points, the interpolation method is used to estimate the value of the target quantile.

7. The method for analyzing and evaluating the resource utilization of the State Grid cloud platform according to claim 1, characterized in that: The constructing of the resource utilization data warehouse comprises: Design resource utilization analysis dimensions, and design resource utilization analysis data drill-down and aggregation rules based on the business requirements of cloud resource operation analysis; Based on the resource utilization analysis dimension, resource utilization analysis data drill-down and aggregation rules are designed, and a resource utilization data warehouse is constructed using the first business system data and the cloud resource utilization detail data to perform multi-dimensional analysis of the cloud resource utilization.

8. A system using the method for analyzing and evaluating the utilization rate of the State Grid cloud platform resources as claimed in any one of claims 1 to 7, characterized in that: include: A data collection module, used to collect first business system data and first operation performance indicators; A data analysis module, configured to analyze the first operating performance indicator by using a first indicator quantile algorithm; An analysis and evaluation module, used to construct a State Grid cloud resource utilization analysis and evaluation model and a first utilization scoring rule for the first operating performance indicator to obtain detailed cloud resource utilization data; The data warehouse construction module is used to construct a resource utilization data warehouse using the first business system data and the cloud resource utilization detail data to perform multi-dimensional analysis on the cloud resource utilization.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the State Grid cloud platform resource utilization analysis and evaluation method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for analyzing and evaluating the resource utilization of the State Grid cloud platform as described in any one of claims 1 to 7.

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