Cloud resource configuration optimization method, device and system, electronic equipment and storage medium

Abstract

The invention provides a cloud resource configuration optimization method, device and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring resource utilization data of a cloud host of each production system on a rail transit system; analyzing and processing the resource utilization data to generate a resource optimization suggestion report corresponding to the production system; generating a resource configuration optimization work order based on the resource optimization suggestion report; establishing a simulation test environment of the production system according to the resource configuration optimization work order, and performing a simulation test on the operation condition of the established production system by using simulation data; after passing, generating a cloud arrangement template based on the simulation test environment, and automatically applying the cloud arrangement template to a cloud host of a production system for resource optimization operation of cloud resources; and recording the cloud resource configuration after resource optimization as a resource configuration baseline so as to compare resource occupancy of each production system. According to the invention, the operation health degree of the production system and effective utilization of cloud resources are improved, and the infrastructure and operation and maintenance cost of the cloud platform is reduced.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to a cloud resource configuration optimization method, device, system, electronic device and computer-readable storage medium. Background Art

[0002] With the rapid development and maturity of cloud computing and big data technologies, cloud platforms have been widely used in the rail transit industry. As the scale and complexity of rail transit systems continue to increase, the growing amount of data has brought tremendous operating pressure to various production systems. These systems require huge computing and storage resources to process massive amounts of data. Although the introduction of cloud computing technology has provided a new solution for resource management of rail transit systems, the cloud application of cloud computing technology in the rail transit industry is still in its early stages. Many city subways lack evaluation methods and standards for the resources required for cloud operation of production systems, which cannot guarantee the stable operation of various production systems, are prone to accidents, and there is a lot of redundancy when reporting the required virtual resources, which has brought tremendous pressure on the construction cost of cloud platforms.

[0003] In related technologies, cloud platform management usually focuses on resource monitoring and basic resource management, and lacks monitoring and evaluation of the overall resource operation of the rail transit production system. Resource allocation depends on initial configuration and manual adjustment, and resource adjustment may introduce new problems, resulting in unstable system operation, thereby increasing the risk of failure. Especially when the rail transit production system needs to adjust resource configuration, due to the lack of a unified rapid deployment mechanism, operation and maintenance personnel need to spend a lot of time and energy to prepare the resources required for the infrastructure, and human errors are prone to occur, increasing operation and maintenance costs.

[0004] Therefore, when adjusting resource allocation, how to improve the utilization rate of cloud platform resources and reduce the cost of cloud platform infrastructure and operation and maintenance is a technical problem that needs to be solved. Summary of the invention

[0005] The present invention provides a cloud resource configuration optimization method, device, system, electronic device and computer-readable storage medium to at least solve the problem in the related art that there is no unified deployment mechanism when adjusting cloud resource configuration, which leads to increased operation and maintenance costs. The technical solution of the present invention is as follows:

[0006] According to a first aspect of an embodiment of the present invention, a cloud resource configuration optimization method is provided, comprising:

[0007] Obtain resource utilization data of cloud hosts of various production systems on the rail transit system;

[0008] Analyze and process the resource utilization data to generate a resource optimization recommendation report for the corresponding production system;

[0009] Generate a resource configuration optimization work order based on the resource optimization suggestion report;

[0010] According to the resource configuration optimization work order, a simulation test environment corresponding to the production system is built, and the operation of the built production system is simulated and tested using simulation data;

[0011] After the simulation test passes, a cloud orchestration template is generated based on the simulation test environment and automatically deployed to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources;

[0012] The cloud resource configuration after resource optimization of the production system is recorded as a resource configuration baseline to facilitate comparison of resource usage of each production system.

[0013] Optionally, before obtaining the resource utilization data of the cloud hosts of each production system on the rail transit system, the method further includes:

[0014] Collect resource utilization data of each cloud host in the rail transit system in real time;

[0015] Performing validity verification on the resource utilization data of the line production system of each cloud host collected in real time;

[0016] The resource utilization data that has been verified to be valid is preprocessed, and the preprocessed resource utilization data is stored in a database.

[0017] Optionally, the analyzing and processing the resource utilization data to generate a resource optimization recommendation report for the corresponding production system includes:

[0018] Analyzing the resource utilization data of the cloud hosts of each production system using a rule-based resource optimization analysis method or a trend prediction-based resource optimization analysis method;

[0019] Determine bottlenecks and excess points in resource utilization of each production system based on the analysis results;

[0020] A resource optimization suggestion report for the corresponding production system is generated based on the bottleneck points and the excess points. The resource optimization suggestion report includes: resource analysis results and resource optimization suggestion measures.

[0021] Optionally, generating a resource configuration optimization work order based on the resource optimization suggestion report includes:

[0022] Generate a corresponding resource configuration optimization work order based on the resource analysis results and resource optimization recommended measures in the resource optimization recommendation report. The resource configuration optimization work order includes: setting up a standardized resource optimization process. The resource optimization process includes: simulation environment construction, simulation detection execution, cloud orchestration template preparation, system and environment backup, resource configuration change, system switching operation, optimization effect verification and system baseline update;

[0023] The resource configuration optimization work orders are managed and tracked to ensure that the resource optimization suggestions can be effectively implemented.

[0024] Optionally, the optimizing work order according to the resource configuration, building a simulation test environment corresponding to the production system, and using simulation data to perform a simulation test on the operation of the built production system, includes:

[0025] According to the resource configuration optimization work order, the production system corresponding to the cloud host is issued in batches, and a simulation environment identical to the optimized production environment is built;

[0026] Using the detection tools of each production system to generate simulation data, simulate the actual operation of the production system, and perform simulation performance testing;

[0027] Using the production system standard cases in the test case library in the simulation environment, testing the production system in the simulation environment, and monitoring the performance and stability of the production system;

[0028] Based on the test results, a simulation test report is generated, wherein the simulation test report includes: performance and risk assessment, and feasibility results of resource optimization.

[0029] Optionally, after the simulation test passes, a cloud orchestration template is generated based on the simulation test environment and automatically deployed to a cloud host of a corresponding production system to perform a resource optimization operation on cloud resources, including:

[0030] After the simulation test passes, an optimized resource configuration declaration file is generated based on the simulation test environment using the infrastructure as code technology and the Terraform template. The resource configuration declaration file includes: all cloud resources used by the production system;

[0031] Use the Terraform template to create a resource-optimized execution plan;

[0032] Running the execution plan will apply complex resource changes to the cloud host of the cloud platform corresponding to the production system, and perform resource optimization operations on the cloud resources.

[0033] Optionally, after the simulation test passes, generating a cloud orchestration template based on the simulation test environment, and automatically deploying the template to a cloud host of a corresponding production system to perform a resource optimization operation on cloud resources, further comprising:

[0034] Before running the execution plan, back up the resources, programs and data of the cloud environment;

[0035] During the resource optimization operation of the execution plan, if an exception occurs, the configuration before the resource optimization is automatically rolled back to maintain stable operation of the system.

[0036] According to a second aspect of an embodiment of the present invention, there is provided a cloud resource configuration optimization device, comprising:

[0037] The acquisition module is used to obtain the resource utilization data of the cloud host of each production system in the rail transit system;

[0038] A processing module, used to analyze and process the resource utilization data and generate a resource optimization recommendation report for the corresponding production system;

[0039] A generation module, used to generate a resource configuration optimization work order based on the resource optimization suggestion report;

[0040] A simulation module is used to optimize the work order according to the resource configuration, build a simulation test environment for the corresponding production system, and use simulation data to simulate the operation of the built production system;

[0041] A cloud orchestration module is used to generate a cloud orchestration template based on the simulation test environment after the simulation test passes, and automatically deploy it to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources;

[0042] The recording module is used to record the cloud resource configuration of the production system after resource optimization as a resource configuration baseline, so as to compare the resource usage of each production system.

[0043] Optionally, the device further comprises: a collection module, a verification module and a preprocessing module, wherein:

[0044] A collection module, used for collecting resource utilization data of each cloud host on the rail transit system in real time before the acquisition module acquires the resource utilization data of the cloud host of each production system on the rail transit system;

[0045] A verification module, used to verify the validity of the resource utilization data of the cloud hosts of each production system collected in real time by the collection module;

[0046] The preprocessing module is used to preprocess the resource utilization data that is verified to be valid by the verification module, and store the preprocessed resource utilization data in a database.

[0047] Optionally, the processing module includes:

[0048] An analysis module, configured to analyze resource utilization data of the cloud hosts of each production system using a rule-based resource optimization analysis method or a trend prediction-based resource optimization analysis method;

[0049] A determination module, used to determine bottleneck points and excess points in resource utilization of each production system based on the analysis results;

[0050] The resource optimization generation module is used to generate a resource optimization suggestion report for the corresponding production system based on the bottleneck points and excess points, and the resource optimization suggestion report includes: resource analysis results and resource optimization suggestion measures.

[0051] Optionally, the generating module includes:

[0052] The optimization work order production module is used to generate a corresponding resource configuration optimization work order according to the resource analysis results and resource optimization recommended measures in the resource optimization recommendation report. The resource configuration optimization work order includes: setting up a standardized resource optimization process, and the resource optimization process includes: simulation environment construction, simulation detection execution, cloud orchestration template preparation, system and environment backup, resource configuration change, system switching operation, optimization effect verification and system baseline update;

[0053] The management and tracking module is used to manage and track the resource configuration optimization work order generated by the optimization work order production module to ensure that the resource optimization suggestions can be effectively implemented.

[0054] Optionally, the simulation module includes:

[0055] A building module is used to optimize the work orders according to the resource configuration, batch-issue the production systems corresponding to the cloud hosts, and build a simulation environment identical to the optimized production environment;

[0056] A simulation module is used to generate simulation data using the detection tools of each production system, simulate the actual operation of the production system, and perform simulation performance testing;

[0057] A simulation test module is used to use the production system standard test cases in the test case library in the simulation environment to test the production system in the simulation environment and monitor the performance and stability of the production system;

[0058] The simulation test report generation module is used to generate a simulation test report based on the test results, wherein the simulation test report includes: performance and risk evaluation, and feasibility results of resource optimization.

[0059] Optionally, the cloud orchestration module includes:

[0060] A resource configuration file generation module is used to generate an optimized resource configuration declaration file based on the simulation test environment by using the infrastructure as code technology and the Terraform template after the simulation test passes. The resource configuration declaration file includes: all cloud resources used by the production system;

[0061] A resource optimization creation module, used to create a resource optimization execution plan using the Terraform template;

[0062] The resource optimization application module is used to run the execution plan, apply complex resource changes to the cloud host of the production system corresponding to the cloud platform, and perform resource optimization operations on the cloud resources.

[0063] Optionally, the cloud orchestration module further includes:

[0064] A backup module, used to back up the resources, programs and data of the cloud environment before running the execution plan;

[0065] The rollback module is used to automatically roll back to the configuration before resource optimization if an exception occurs during the resource optimization operation of the execution plan, so as to maintain stable operation of the system.

[0066] According to a third aspect of an embodiment of the present invention, a cloud resource configuration optimization system is provided, including:

[0067] The monitoring agent module is used to collect resource utilization data of the cloud hosts of various production systems in the rail transit system in real time;

[0068] The data access service module is used to verify the validity of the resource utilization data and perform preprocessing to ensure the accuracy and completeness of the resource utilization data; and store the preprocessed resource utilization data in the database;

[0069] A cloud resource monitoring module, used to obtain resource utilization data of cloud hosts of each production system from the database;

[0070] An intelligent analysis module, used to analyze and process the real-time resource utilization data and historical resource utilization data obtained by the cloud resource monitoring module, and generate a resource optimization recommendation report for the corresponding production system;

[0071] A work order management module, used to generate a resource configuration optimization work order based on the resource optimization suggestion report;

[0072] A simulation detection module is used to optimize the work order according to the resource configuration, build a simulation test environment for the corresponding production system, and use simulation data to simulate the operation of the built production system;

[0073] A cloud orchestration module is used to generate a cloud orchestration template based on the simulation test environment after the simulation test passes, and automatically deploy it to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources;

[0074] The baseline module is used to record the cloud resource configuration after resource optimization of the production system as a resource configuration baseline, so as to compare the resource usage of each production system.

[0075] According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device, including:

[0076] processor;

[0077] a memory for storing instructions executable by the processor;

[0078] The processor is configured to execute the instructions to implement the cloud resource configuration optimization method as described above.

[0079] According to a fifth aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the cloud resource configuration optimization method as described above.

[0080] According to a sixth aspect of an embodiment of the present invention, there is provided a computer program product, comprising a computer program or instructions, which, when executed by a processor of an electronic device, implements the cloud resource configuration optimization method as described above.

[0081] The technical solution provided by the embodiments of the present invention brings at least the following beneficial effects:

[0082] In an embodiment of the present invention, resource utilization data of cloud hosts of various production systems on a rail transit system are obtained; the resource utilization data are analyzed and processed to generate a resource optimization recommendation report for the corresponding production system; a resource configuration optimization work order is generated based on the resource optimization recommendation report; according to the resource configuration optimization work order, a simulation test environment for the corresponding production system is built, and simulation data is used to perform a simulation test on the operation of the built production system; after the simulation test passes, a cloud orchestration template is generated based on the simulation test environment, and is automatically deployed to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources; the cloud resource configuration after resource optimization of the production system is recorded as a resource configuration baseline to facilitate subsequent comparison of resource occupancy of each production system. That is to say, in the embodiment of the present invention, by monitoring and counting the cloud resource utilization rates of the cloud hosts of each production system, analysis reports are regularly generated, the system proactively discovers points that can be optimized (such as expansion or reduction), and then quickly builds a simulation environment and uses simulated data to test the operation of the production system to generate a test report. After the simulation test passes, the cloud orchestration function can be used to quickly pull up the cloud environment and switch to the production system operation, thereby improving the health of the production system operation and the effective use of the overall cloud platform resources, reducing the cloud platform infrastructure and operation and maintenance costs.

[0083] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention, and do not constitute an improper limitation of the present invention. In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0085] Figure 1 It is a flow chart of a cloud resource configuration optimization method provided by an embodiment of the present invention.

[0086] Figure 2 It is a block diagram of a cloud resource configuration optimization device provided by an embodiment of the present invention.

[0087] Figure 3 It is another block diagram of a cloud resource configuration optimization device provided by an embodiment of the present invention.

[0088] Figure 4It is a block diagram of a processing module provided by an embodiment of the present invention.

[0089] Figure 5 A block diagram of a generation module provided by an embodiment of the present invention

[0090] Figure 6 It is a block diagram of a simulation module provided by an embodiment of the present invention.

[0091] Figure 7 It is a block diagram of a cloud resource configuration optimization system provided by an embodiment of the present invention.

[0092] Figure 8 It is a block diagram of an electronic device provided by an embodiment of the present invention.

[0093] Fig. 9 It is a block diagram of a device for optimizing cloud resource configuration provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0094] In order to enable ordinary persons in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0095] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0096] Figure 1 is a flow chart of a cloud resource configuration optimization method provided by an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0097] Step 101: Obtain resource utilization data of the cloud hosts of each production system in the rail transit system.

[0098] Step 102: Analyze and process the resource utilization data to generate a resource optimization recommendation report for the corresponding production system.

[0099] Step 103: Generate a resource configuration optimization work order based on the resource optimization suggestion report.

[0100] Step 104: According to the resource configuration optimization work order, a simulation test environment corresponding to the production system is built, and simulation data is used to perform simulation testing on the operation of the built production system.

[0101] Step 105: After the simulation test passes, a cloud orchestration template is generated based on the simulation test environment and automatically deployed to the cloud host of the corresponding production system to perform resource optimization operations on the cloud resources.

[0102] Step 106: Record the cloud resource configuration after resource optimization of the production system as a baseline to facilitate subsequent comparison of resource usage of each production system.

[0103] The cloud resource configuration optimization method described in the present invention can be applied to terminals, servers, etc., without limitation herein. The terminal implementation device can be electronic devices such as smart phones, laptops, tablet computers, desktop computers, personal digital assistants (PDAs) and wearable devices. The server can be an independent server or a server cluster, or a server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, intermediate services, domain name services, security services, content distribution networks, or big data and artificial intelligence platforms, etc., without limitation herein.

[0104] Combine the following Figure 1 , the specific implementation steps of a cloud resource configuration optimization method provided by an embodiment of the present invention are described in detail.

[0105] In step 101, resource utilization data of cloud hosts of various production systems in the rail transit system is obtained.

[0106] In this step, the resource utilization data of the cloud hosts of each production system in the rail transit system can be obtained from the database, and the resource utilization of each cloud host can be monitored in real time, and historical data can be recorded and displayed in the form of charts.

[0107] Among them, in this step, there are two ways of obtaining. Cloud resource monitoring can retrieve the cloud resource utilization of the production system from two ways: line priority or production system priority, and obtain the resource utilization data of the cloud hosts of each production system on the rail transit system. Furthermore, based on the resource utilization data, real-time monitoring reports and resource utilization trend charts can be generated. The resource utilization data may include: the overall occupancy of production system resources, the CPU utilization, memory utilization, GPU utilization and disk space of each cloud host, etc., and a preliminary analysis of the resource utilization data can be performed.

[0108] In this step, the storage and query of historical resource utilization data are supported. Users can retrieve the stored resource utilization data within a certain period of time in the past to facilitate subsequent analysis and use.

[0109] It should be noted that in this step, before acquisition, the database has already stored the resource utilization data of the cloud hosts of various production systems on the rail transit system, wherein the resource utilization data is stored by the data access service. Specifically, the obtained resource utilization data of the cloud hosts of the various production systems are verified for validity; and the verified valid resource utilization data is preprocessed (including format conversion and cleaning) to generate basic data reports and display them in the form of charts.

[0110] That is to say, the data access service obtains the resource utilization data detected by each monitoring agent from the message queue (such as the Kafka message queue, etc.), verifies the validity of the data, and performs preliminary processing (such as necessary data format conversion, etc.) and cleaning on the resource utilization data to ensure the accuracy and completeness of the data. After that, the processed resource utilization data is transmitted to the database for storage.

[0111] After preliminary processing and cleaning, it may also include: generating a basic data report based on the resource utilization data after preliminary processing and cleaning, and storing the basic data report in a database for subsequent cloud resource monitoring.

[0112] Among them, the data access service can sense the status of each monitoring agent through the heartbeat mechanism to ensure stable operation of data collection.

[0113] It should be noted that the monitoring agent is deployed on each cloud host of the rail transit system and is responsible for collecting the resource utilization of each cloud host in real time, including key indicators such as CPU utilization, memory utilization, and GPU utilization. The monitoring agent sends the collected resource utilization data to the message queue for easy use of the data access service.

[0114] Among them, each monitoring agent configures the resource utilization data items and collection frequency to be collected through system calls and performance monitoring tools; after startup, the resource utilization data of the cloud host is collected in real time, and the collected resource utilization data is packaged every minute and sent to the Kafka message queue. The message sent by the monitoring agent includes: cloud host ID, the line and production system to which it belongs, IP address, data collection timestamp within the cycle, and specific values ​​of each monitoring item. The monitoring agent can set the target address and topic (Topic) for sending the message packet according to the line production system to which the monitored cloud host belongs, and the data access service obtains the resource utilization data from the corresponding Topic, on the one hand, performs preliminary processing and request, and on the other hand, can also be transmitted to other systems (such as central systems, etc.) for processing and analysis, which is not limited in this embodiment.

[0115] In step 102, the resource utilization data is analyzed and processed to generate a resource optimization recommendation report corresponding to the production system.

[0116] In this step, the resource utilization data of the cloud hosts of the various production systems are analyzed by a rule-based resource optimization analysis method or a trend prediction-based resource optimization analysis method; the bottleneck points and excess points in the resource utilization of each production system are determined based on the analysis results; and a resource optimization recommendation report for the corresponding production system is generated based on the bottleneck points and excess points, and the resource optimization recommendation report includes: resource analysis results and resource optimization recommendation measures.

[0117] That is to say, in this embodiment, the collected resource utilization data of each cloud host is deeply analyzed to determine the bottlenecks and excess points in resource utilization, and generate a resource optimization recommendation report, wherein the resource optimization recommendation report includes: resource analysis results and resource optimization recommendation measures, that is, an analysis report is generated, optimization recommendation measures are given, and the process and results of analyzing the resource utilization data are displayed in the form of statistical charts and reports.

[0118] Afterwards, real-time and historical resource utilization data can be obtained from the production system cloud resource monitoring and analyzed in the following two modes:

[0119] Rule-based resource optimization: Use predefined rules and policies to optimize resources, set utilization thresholds for various types of resources, and use long-term monitoring data to determine whether resources need to be expanded or reduced.

[0120] Resource optimization based on trend prediction: Use regression analysis algorithms to predict resource demand trends, and discover potential bottlenecks and optimization points for future resource utilization through historical resource monitoring data.

[0121] The system performs intelligent analysis every day, presenting the analysis process and results in the form of statistical charts and reports, and describing the forecast model and resource demand trend chart in detail. Based on the analysis results, resource configuration optimization suggestions are generated, which may include: which line, which cloud host of which production system, which type of resources needs to be increased or reduced, and the recommended configuration adjustment amount, etc., but it is not limited to this in specific applications.

[0122] Based on the above analysis, intelligent analysis will regularly output optimization recommendation reports, which include: detailed analysis results and recommended optimization measures (i.e. resource analysis results and resource optimization recommended measures), and provide them to work order management to create resource optimization work orders.

[0123] In step 103, a resource configuration optimization work order is generated based on the resource optimization suggestion report.

[0124] In this step, a resource configuration optimization work order can be generated based on the optimization suggestions of the above intelligent analysis, and the resource configuration optimization work order can be managed and tracked to ensure that the optimization suggestions can be effectively implemented.

[0125] That is to say, the work order management receives optimization suggestions from the intelligent analysis module and generates corresponding resource configuration optimization work orders. A standardized resource optimization process is set up in the resource configuration optimization work order, which includes multiple steps: simulation environment construction, simulation detection execution, cloud orchestration template preparation, system and environment backup, resource configuration change, system switching operation, optimization effect verification, and system baseline update. The resource configuration optimization work order can display the specific execution steps and expected results of these nodes in the details. The progress of each step can be entered manually, or the system can automatically synchronize the execution results, track the execution progress of the resource configuration optimization work order, and ensure that resource optimization is completed as planned.

[0126] In step 104, a simulation test environment of the corresponding production system is built according to the resource configuration optimization work order, and simulation data is used to perform simulation test on the operation of the built production system.

[0127] In this step, according to the resource configuration optimization work order, it is sent in batches to the cloud host of the corresponding production system, and a simulation environment identical to the optimized production environment is built; the detection tools of each production system are used to generate simulation data, simulate the actual operation of the production system, and perform simulation performance testing; in the simulation environment, the production system in the simulation environment is tested using the standard test cases of the production system in the test case library to monitor the performance and stability of the production system; based on the test results, a simulation test report is generated, which includes: performance and risk assessment, and feasibility results of determining resource optimization.

[0128] In this step, according to the optimization suggestions provided in the resource allocation optimization work order, a simulation environment is quickly built, and simulation tests are performed using simulated data to ensure the feasibility and effectiveness of the optimization solution. The simulation test specifically includes:

[0129] Simulation environment construction: Optimize work orders according to resource allocation, issue cloud hosts in batches, and build a simulation environment that is identical to the optimized production environment.

[0130] Simulation data generation: Use the detection tools of each production system to generate simulation data, simulate the actual operation situation, and conduct performance testing.

[0131] Test execution: Use the production system standard test cases in the test case library in the simulation environment to test the production system in the simulation environment and monitor its performance and stability.

[0132] Test report generation: Analyze test results, generate simulation test reports, issue detailed performance and risk assessments, and determine the feasibility and effectiveness of resource optimization solutions, that is, the feasibility results of resource optimization.

[0133] In step 105, after the simulation test passes, a cloud orchestration template is generated based on the simulation test environment and automatically deployed to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources.

[0134] In this step, after the simulation test passes, infrastructure as code is used to generate an optimized resource configuration declaration file based on the simulation test environment through the Terraform template. The resource configuration declaration file includes: all cloud resources used by the production system; using the Terraform template to create an execution plan for resource optimization; running the execution plan will apply complex resource changes to the cloud host of the cloud platform corresponding to the production system, and perform resource optimization operations on cloud resources.

[0135] That is to say, in this step, after the simulation test passes, a cloud orchestration template is generated according to the simulation test environment, automatically deployed to the cloud platform, and specific resource optimization operations are performed. Specifically:

[0136] You can use Infrastructure-as-Code (IaC) technology to generate an optimized resource configuration declaration file based on the simulation test environment through Terraform templates. The resource configuration declaration file can include all cloud resources used by the production system. Then, you can use Terraform templates to create an execution plan, which shows what the system will do when the update is implemented, so as to check in advance and avoid unexpected operations. After that, run the execution plan to apply complex resource changes to the cloud platform without manual interaction.

[0137] Furthermore, this step may also include: before running the execution plan, backing up the resources, programs and data of the cloud environment; during the resource optimization operation of the execution plan, if an abnormality occurs, automatically rolling back to the configuration before resource optimization to maintain stable operation of the system.

[0138] That is, before running the execution plan, the system will automatically package and back up the resources, programs and data of the cloud environment. In order to automatically roll back to the previous configuration when problems occur during the execution plan, the system can maintain stable operation.

[0139] Furthermore, when using the template to perform resource adjustment operations, a specific event log will be generated to record the detailed execution process and results of each step, and the results will be fed back to the resource configuration optimization work order for management.

[0140] It should be noted that Terraform is an open source tool for previewing, configuring and managing cloud infrastructure and resources safely and efficiently. It can also be understood as an IAC (Infrustracture as Code) tool that can operate various resources on the cloud and physical machines by writing easy-to-read code. In the resource life cycle, they can be managed uniformly through a unified workflow.

[0141] Infrastructure as Code (IaC) is the use of code to define and manage infrastructure instead of manual processes. It should be noted that IaC is the introduction of software engineering principles and methods into cloud infrastructure.

[0142] In step 106, the cloud resource configuration of the production system after resource optimization is recorded as a resource configuration baseline, so as to facilitate subsequent comparison of resource usage of each production system.

[0143] In this step, the cloud resource configuration after optimization of the production system is defined as the resource configuration baseline to maintain the benchmark configuration and standards for system operation, summarize the cloud platform management experience, and provide a resource configuration reference for new lines entering the cloud.

[0144] That is to say, first summarize the resource configuration baselines of each production system and display them. The resource configuration baseline can include basic information such as lines, production systems, baseline versions, baseline updaters and update time; of course, the resource configuration baseline can also include: the number of cloud hosts (such as servers) that need to be deployed in the production system, and the resource configuration of each cloud host (server), and classify and count the total cloud resource demand of the production system.

[0145] Initially, users can define the system baseline based on the resource requirements proposed by professional system suppliers. During the operation of the production system, users can define the current resource configuration of the system as the resource configuration baseline based on the actual resource utilization.

[0146] Resource configuration baselines support multi-version management. After each optimization operation is verified, users use the resource configuration baseline update function to update the baseline configuration of the line production system and record the new resource usage standards. In resource configuration baseline version management, you can retrieve historical versions of cloud resource baseline configurations. At the same time, the peak and average resource utilization of each version of the baseline configuration is recorded on a monthly basis to evaluate the accuracy of the resource configuration baseline.

[0147] Resource configuration baseline management has a comparison function, which supports comparison between different versions of the production system baseline, comparison between baselines of different lines in the same production system, and comparison between the baseline and the current system operation resource configuration, and analyzes the compliance of system operation and baseline deviation.

[0148] In an embodiment of the present invention, resource utilization data of cloud hosts of various production systems on a rail transit system are obtained; the resource utilization data are analyzed and processed to generate a resource optimization recommendation report for the corresponding production system; a resource configuration optimization work order is generated based on the resource optimization recommendation report; according to the resource configuration optimization work order, a simulation test environment for the corresponding production system is built, and simulation data is used to perform a simulation test on the operation of the built production system; after the simulation test passes, a cloud orchestration template is generated based on the simulation test environment, and is automatically deployed to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources; the cloud resource configuration after resource optimization of the production system is recorded as a resource configuration baseline to facilitate subsequent comparison of resource occupancy of each production system. That is to say, in the embodiment of the present invention, by monitoring and counting the utilization rates of cloud resources (such as CPU, memory, GPU, etc.) used by the cloud hosts of each production system, analysis reports are regularly generated, and the system proactively discovers points that can be optimized (such as expansion or reduction), and then quickly builds a simulation environment and uses simulated data to test the operation of the production system to generate a test report. After the simulation test passes, the cloud orchestration function can be used to quickly pull up the cloud environment and switch to the production system operation, thereby improving the health of the production system operation and the effective use of the overall cloud platform resources, reducing the cloud platform infrastructure and operation and maintenance costs.

[0149] It should be noted that, for the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that this implementation disclosure is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the present invention.

[0150] See also Figure 2 , is a block diagram of a cloud resource configuration optimization device provided by an embodiment of the present invention. The device includes: an acquisition module 201, a processing module 202, a generation module 203, a simulation module 204, a cloud orchestration module 205 and a recording module 206, wherein:

[0151] The acquisition module 201 is used to acquire the resource utilization data of the cloud host of each production system in the rail transit system;

[0152] The processing module 202 is used to analyze and process the resource utilization data to generate a resource optimization recommendation report for the corresponding production system;

[0153] A generating module 203 is used to generate a resource configuration optimization work order based on the resource optimization suggestion report;

[0154] The simulation module 204 is used to optimize the work order according to the resource configuration, build a simulation test environment corresponding to the production system, and use simulation data to simulate the operation of the built production system;

[0155] The cloud orchestration module 205 is used to generate a cloud orchestration template based on the simulation test environment after the simulation test passes, and automatically deploy it to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources;

[0156] The recording module 206 is used to record the cloud resource configuration after resource optimization of the production system as a resource configuration baseline, so as to compare the resource usage of each production system.

[0157] Optionally, in another embodiment, based on the above embodiment, the device further includes: a collection module 301, a verification module 302 and a pre-processing module 303, and its structural block diagram is as follows: Figure 3 As shown,

[0158] The collection module 301 is used to collect the resource utilization data of each cloud host in the rail transit system in real time before the acquisition module 201 acquires the resource utilization data of the cloud host of each production system in the rail transit system;

[0159] A verification module 302 is used to verify the validity of the resource utilization data of the cloud hosts of each production system collected in real time by the collection module;

[0160] The preprocessing module 303 is used to preprocess the resource utilization data verified to be valid by the verification module 301, and store the preprocessed resource utilization data in a database.

[0161] Optionally, in another embodiment, based on the above embodiment, the processing module 202 includes: an analysis module 401, a determination module 402 and a resource optimization generation module 403, and its structural block diagram is as follows: Figure 4 As shown,

[0162] An analysis module 401 is used to analyze the resource utilization data of the cloud hosts of each production system using a rule-based resource optimization analysis method or a trend prediction-based resource optimization analysis method;

[0163] A determination module 402, for determining bottleneck points and excess points in resource utilization of each production system according to the analysis results;

[0164] The resource optimization generation module 403 is used to generate a resource optimization suggestion report for the corresponding production system based on the bottleneck points and the excess points. The resource optimization suggestion report includes: resource analysis results and resource optimization suggestion measures.

[0165] Optionally, in another embodiment, based on the above embodiment, the generation module 203 includes: an optimization work order production module 501 and a management tracking module 502, whose structural block diagram is as follows: Figure 5 As shown,

[0166] The optimization work order production module 501 is used to generate a corresponding resource configuration optimization work order according to the resource analysis results and resource optimization recommended measures in the resource optimization recommendation report. The resource configuration optimization work order includes: setting up a standardized resource optimization process, and the resource optimization process includes: simulation environment construction, simulation detection execution, cloud orchestration template preparation, system and environment backup, resource configuration change, system switching operation, optimization effect verification and system baseline update;

[0167] The management and tracking module 502 is used to manage and track the resource configuration optimization work order generated by the optimization work order generation module 501 to ensure that the resource optimization suggestions can be effectively implemented.

[0168] Optionally, in another embodiment, based on the above embodiment, the simulation module 204 includes: a building module 601, a simulation module 602, a simulation test module 603 and a simulation test report generation module 604, and its structural block diagram is as follows: Figure 6 As shown,

[0169] A building module 601 is used to optimize the work orders according to the resource configuration, batch-send them to the cloud host of the corresponding production system, and build a simulation environment that is the same as the optimized production environment;

[0170] The simulation module 602 is used to generate simulation data using the detection tools of each production system, simulate the actual operation of the production system, and perform simulation performance testing;

[0171] A simulation test module 603 is used to test the production system in the simulation environment using the production system standard test cases in the test case library in the simulation environment to monitor the performance and stability of the production system;

[0172] The simulation test report generating module 604 is used to generate a simulation test report based on the test results, wherein the simulation test report includes: performance and risk evaluation, and feasibility results of resource optimization.

[0173] Optionally, in another embodiment, based on the above embodiment, the cloud orchestration module includes: a resource configuration file generation module, a resource optimization creation module and a resource optimization application module, wherein:

[0174] A resource configuration file generation module is used to generate an optimized resource configuration declaration file based on the simulation test environment by using the infrastructure as code technology and the Terraform template after the simulation test passes. The resource configuration declaration file includes: all cloud resources used by the production system;

[0175] A resource optimization creation module, used to create a resource optimization execution plan using the Terraform template;

[0176] The resource optimization application module is used to run the execution plan, apply complex resource changes to the cloud host of the production system corresponding to the cloud platform, and perform resource optimization operations on the cloud resources.

[0177] Optionally, in another embodiment, based on the above embodiment, the cloud orchestration module may further include: a backup module and a rollback module, wherein:

[0178] A backup module, used to back up the resources, programs and data of the cloud environment before running the execution plan;

[0179] The rollback module is used to automatically roll back to the configuration before resource optimization if an exception occurs during the resource optimization operation of the execution plan, so as to maintain stable operation of the system.

[0180] Optionally, an embodiment of the present invention further provides an electronic device, including:

[0181] processor;

[0182] a memory for storing instructions executable by the processor;

[0183] The processor is configured to execute the instructions to implement the cloud resource configuration optimization method as described above.

[0184] Optionally, an embodiment of the present invention further provides a computer-readable storage medium, which, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to execute the cloud resource configuration optimization method as described above.

[0185] Optionally, an embodiment of the present invention further provides a computer program product, including a computer program or instructions, which implement the cloud resource configuration optimization method as described above when executed by a processor of an electronic device.

[0186] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0187] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0188] See also Figure 7 , is a structural block diagram of a cloud resource configuration optimization system provided by an embodiment of the present invention, the system includes: a monitoring agent module 701 (this embodiment takes the monitoring agent modules deployed on three production systems as an example, namely, monitoring agent module 1, monitoring agent module 2 and monitoring agent module 3, which are not limited to this in actual applications), a data access service module 702, a cloud resource monitoring module 703, an intelligent analysis module 704, a work order management module 705, a simulation detection module 706, a cloud orchestration module 707 and a baseline module 708, wherein,

[0189] The monitoring agent module 701 (ie each monitoring agent module) is used to collect resource utilization data of the cloud host of each production system in the rail transit system in real time.

[0190] The data access service module 702 is used to verify the validity of the resource utilization data and perform preprocessing to ensure the accuracy and completeness of the resource utilization data; and store the preprocessed resource utilization data in the database.

[0191] The cloud resource monitoring module 703 is used to obtain resource utilization data of the cloud hosts of each production system from the database.

[0192] The cloud resource monitoring module 703 is also used to monitor the resource utilization of each cloud host in real time based on the resource utilization data, record it, and display it in the form of a chart;

[0193] The intelligent analysis module 704 is used to analyze and process the real-time resource utilization data and historical resource utilization data obtained by the cloud resource monitoring module to generate a resource optimization recommendation report for the corresponding production system.

[0194] Among them, the intelligent analysis module is also used to determine the bottleneck points and excess points in the resource utilization of each production system according to the analysis results, and generate a resource optimization recommendation report for the corresponding production system based on the bottleneck points and excess points. The resource optimization recommendation report includes: resource analysis results and resource optimization recommendation measures.

[0195] The work order management module 705 is used to generate a resource configuration optimization work order based on the resource optimization suggestion report, specifically including: generating a corresponding resource configuration optimization work order according to the resource analysis results and resource optimization suggestion measures in the resource optimization suggestion report, and managing and tracking the resource configuration optimization work order to ensure that the resource optimization suggestion can be effectively implemented.

[0196] The simulation detection module 706 is used to optimize the work order according to the resource configuration, build a simulation test environment corresponding to the production system, and use simulation data to simulate the operation of the built production system.

[0197] Specifically, the simulation detection module 706 issues the production systems corresponding to the cloud hosts in batches according to the resource configuration optimization work order, and builds a simulation environment identical to the optimized production environment; uses the detection tools of each production system to generate simulation data, simulates the actual operation of the production system, and performs simulation performance testing; uses the production system standard cases in the test case library in the simulation environment to test the production system in the simulation environment, and monitors the performance and stability of the production system; generates a simulation test report based on the test results, wherein the simulation test report includes a detailed performance and risk assessment, and determines the feasibility and effect of resource optimization;

[0198] The cloud orchestration module 707 is used to generate a cloud orchestration template based on the simulation test environment after the simulation test passes, and automatically deploy it to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources.

[0199] Specifically, after the simulation test passes, the cloud orchestration module 707 uses the infrastructure as code technology and the Terraform template to generate an optimized resource configuration declaration file based on the simulation test environment, wherein the resource configuration declaration file includes: all cloud resources used by the production system; using the Terraform template to create an execution plan for resource optimization; running the execution plan will apply complex resource changes to the cloud host of the production system corresponding to the cloud platform, and perform resource optimization operations on the cloud resources.

[0200] The baseline module 708 is used to record the cloud resource configuration after resource optimization of the production system as a resource configuration baseline, so as to compare the resource usage of each production system.

[0201] Specifically, the baseline module 708 records the cloud resource configuration after resource optimization of the production system as a resource configuration baseline to maintain the benchmark configuration and standard of system operation, so as to compare the resource usage of each production system.

[0202] In the embodiment of the present invention, the implementation process of the functions and effects of each module is specifically described in the specific implementation process of the corresponding steps in the above embodiment, which will not be repeated here.

[0203] In the embodiment of the present invention, the operation data of the cloud resources used by the rail transit production system is summarized as a dimension for unified monitoring and management; the historical data of key indicators such as CPU, memory, and GPU utilization are automatically analyzed using special algorithms, and specific resource allocation optimization suggestions are proposed using LLM technology; simulation detection technology is used in the allocation optimization of cloud resources: a simulation environment is quickly built, and the production system is fully tested using simulated data to ensure the feasibility and effectiveness of the optimization plan; automated cloud orchestration technology is used to quickly optimize the cloud resources of the production system, including expansion, reduction, and switching, to ensure seamless connection and stable operation of the resource adjustment process. And the resource demand configuration of the production system is recorded and stored using a baseline, and the resource occupancy of each line and each production system is compared using the baseline, supporting resource configuration that falls back to a certain historical baseline.

[0204] For ease of understanding, please refer to the following first application example in conjunction with the embodiment of the above system.

[0205] In this embodiment, the resource optimization process is performed by assuming that the CPU utilization of the cloud host of a certain rail transit line is too high. It should be noted that the monitoring agent module needs to be pre-deployed on each cloud host of the rail transit system and is responsible for real-time collection of resource utilization of each cloud host, including key indicators such as CPU utilization, memory utilization, and GPU utilization. The monitoring agent module sends the resource utilization data to the message queue for use by the data access service module. Specifically, it includes:

[0206] The monitoring agent module collects the CPU utilization data of the cloud host in real time and transmits it to the cloud resource monitoring module through the data access service.

[0207] The cloud resource monitoring module generates a real-time monitoring report, showing that the CPU utilization of the cloud host is continuously too high.

[0208] The intelligent analysis module analyzes the data information in the real-time monitoring report, identifies the bottleneck of CPU resource overload, and generates optimization suggestions for capacity expansion.

[0209] The work order management module generates a capacity expansion work order based on the optimization suggestions for capacity expansion and submits it to the simulation detection module.

[0210] The simulation detection module builds a simulation test environment to simulate the operation after capacity expansion. If the test results show that capacity expansion can significantly improve system performance.

[0211] The cloud orchestration module performs actual CPU resource expansion operations based on the simulation results to ensure that the system seamlessly switches to the optimized configuration.

[0212] The baseline management module updates the resource configuration baseline of the production system and records the new resource configuration, which facilitates the comparison of resource usage of each production system and ensures the long-term stable operation of the system.

[0213] The embodiments of the present invention, based on the resource configuration evaluation and optimization system on the rail transit cloud, can efficiently monitor, analyze and optimize the utilization of cloud resources, ensure the efficient operation of the rail transit system and the reasonable configuration of resources, significantly reduce the operation and maintenance costs, and improve the overall operation health of the system.

[0214] For ease of understanding, please refer to the following second application example in conjunction with the embodiment of the above system.

[0215] In this embodiment, it is assumed that a subway system in a certain city has introduced the cloud resource configuration optimization system of the present invention and applied it to the resource configuration evaluation and optimization on the rail transit cloud. The following is a specific example:

[0216] If a subway line (such as Line 20) is already running on the cloud, the line has a total of 10 production systems (this embodiment takes 10 as an example, but is not limited to this in specific applications) that rely on the cloud computing platform to support daily operations, operation management, real-time monitoring and data analysis. The specific application process includes the following 7 parts:

[0217] 1) Data collection and aggregation

[0218] A monitoring agent module is deployed on each production system cloud host of this line. Each monitoring agent module is used to collect resource utilization data of the corresponding cloud host in real time. After that, the monitoring agent module transmits the collected data utilization data to the data access service module through the message queue.

[0219] 2) Data processing and intelligent analysis

[0220] The data access service module performs preliminary processing and cleaning (preprocessing) on ​​the resource utilization data from the 10 production systems, generates basic data reports, and displays them in the form of charts in the cloud resource monitoring module of the production system. The intelligent analysis module obtains the preprocessed data from the data access service module and performs in-depth statistical and trend analysis. Assume that the analysis results show:

[0221] The CPU utilization rate of Server A (cloud host) of the Line 20 integrated monitoring system has been above 85% for a long time, with occasional performance degradation and slow response problems.

[0222] The memory utilization rate of Server B (cloud host) of the Line 20 passenger information service system is less than 20%, and memory resources are seriously idle.

[0223] 3) Optimization suggestion generation

[0224] Based on the analysis results, the intelligent analysis module puts forward the following specific optimization suggestions:

[0225] Increase the number of CPU cores of Server A of Line 20 Integrated Monitoring System to 16 cores.

[0226] Reduce the memory allocation of the Line 20 Passenger Information Service System B server to 8GB.

[0227] 4) Create a resource optimization work order

[0228] The work order management module receives optimization suggestions from the intelligent analysis module and generates resource allocation optimization work orders for the Line 20 integrated monitoring system and the passenger information service system after confirmation by the user.

[0229] Users set the planned execution time for each link of the work order in order to track the execution progress and effect of the resource allocation optimization work order and ensure the orderly implementation of the optimization measures.

[0230] 5) Simulation detection

[0231] The simulation detection module quickly builds a simulation environment for the corresponding production system based on resource allocation and work order optimization:

[0232] Line 20 integrated monitoring system: With other resource configurations unchanged, the number of CPU cores of server A is adjusted to 16 cores;

[0233] Line 20 Passenger Information Service System: With other resource configurations unchanged, the memory allocation of server B is adjusted to 8GB.

[0234] The simulation detection module quickly deploys the production system in the simulation environment through mirroring, and uses simulated data to perform performance testing, stability testing, and load testing on the production system in the simulation environment according to the corresponding case scripts in the test case library. The test results show that the optimization solution can significantly improve the operating performance of the Line 20 integrated management system without negatively affecting the passenger information service system.

[0235] The simulation detection module generates a test report, indicating the test results, to ensure the feasibility and effectiveness of the optimization plan.

[0236] 6) Resource optimization implementation

[0237] After the test report of the simulation detection module is passed, the cloud orchestration module generates the corresponding template according to the simulation environment, quickly builds the adjusted cloud environment, increases the number of CPU cores of Server A of the Line 20 integrated monitoring system, and reduces the memory allocation of Server B of the passenger information service system.

[0238] The system automatically backs up the components and programs of the Line 20 integrated management system and passenger information service system, and deploys them to the new operating environment. After the operation and maintenance personnel check that they are correct, they switch to the production system in the new operating environment to avoid affecting the system operation. Among them, the cloud orchestration module also includes a fault processor, which is used to monitor the complete optimization implementation process. During the optimization implementation process, if a fault occurs, it will automatically roll back to the previous configuration.

[0239] 7) Effect verification and baseline establishment

[0240] After a period of actual operation, the monitoring agent module continues to collect data in real time and transmits it to the data access service module. The intelligent analysis module analyzes resource utilization again to verify the optimization effect:

[0241] Line 20 integrated monitoring system: After optimization, CPU utilization was reduced to about 60%, system performance was significantly improved, and operational stability was enhanced.

[0242] Line 20 passenger information service system: After optimization, memory utilization rate increased to about 50%, memory resources were used more effectively, and resource waste was reduced.

[0243] The user uses the baseline management module to establish new baselines (i.e. resource configuration baselines) for the two production systems, record the change history of cloud resource configuration, and accumulate cloud platform management experience. The system continuously cycles through the above steps to continuously optimize the cloud platform resource configuration.

[0244] It can be seen from the above application examples that the cloud resource configuration optimization system provided by the embodiment of the present invention performs on-line resource configuration evaluation and optimization of rail transit, which not only improves resource utilization and reduces operation and maintenance costs, but also improves the response speed and operation health of the cloud platform.

[0245] Figure 8 800 is a block diagram of an electronic device 800 provided in an embodiment of the present invention. For example, the electronic device 800 may be a mobile terminal or a server, and the electronic device is a mobile terminal as an example for explanation in the embodiment of the present invention. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a message transceiver device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0246] Reference Figure 8 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0247] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0248] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0249] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0250] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and the rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0251] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0252] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0253] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0254] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0255] In an embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the cloud resource configuration optimization method shown above.

[0256] In an embodiment, a computer-readable storage medium is also provided, and when the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device 800 can perform the cloud resource configuration optimization method shown above. For example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0257] In an embodiment, a computer program product is also provided, including a computer program or instructions. When the computer program or instructions are executed by the processor 820 of the electronic device 800, the electronic device 800 executes the above-mentioned cloud resource configuration optimization method.

[0258] Fig. 9 is a block diagram of a device 900 for optimizing cloud resource configuration provided by an embodiment of the present invention. For example, the device 900 may be provided as a server. Fig. 9 , the apparatus 900 includes a processing component 922, which further includes one or more processors, and a memory resource represented by a memory 932 for storing instructions, such as an application, that can be executed by the processing component 922. The application stored in the memory 932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 922 is configured to execute the instructions to perform the above method.

[0259] The device 900 may also include a power supply component 926 configured to perform power management of the device 900, a wired or wireless network interface 950 configured to connect the device 900 to a network, and an input / output (I / O) interface 958. The device 900 may operate based on an operating system stored in the memory 932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0260] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0261] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A cloud resource configuration optimization method, characterized in that: include: Obtain resource utilization data of cloud hosts of various production systems on the rail transit system; Analyze and process the resource utilization data to generate a resource optimization recommendation report for the corresponding production system; Generate a resource configuration optimization work order based on the resource optimization suggestion report; According to the resource configuration optimization work order, a simulation test environment corresponding to the production system is built, and the operation of the built production system is simulated and tested using simulation data; After the simulation test passes, a cloud orchestration template is generated based on the simulation test environment and automatically deployed to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources; The cloud resource configuration after resource optimization of the production system is recorded as a resource configuration baseline to facilitate comparison of resource usage of each production system.

2. The cloud resource configuration optimization method according to claim 1, characterized in that: Before obtaining the resource utilization data of the cloud hosts of each production system on the rail transit system, the method further includes: Collect resource utilization data of each cloud host in the rail transit system in real time; Performing validity verification on the resource utilization data of the line production system of each cloud host collected in real time; The resource utilization data that has been verified to be valid is preprocessed, and the preprocessed resource utilization data is stored in a database.

3. The cloud resource configuration optimization method according to claim 1, characterized in that: The analyzing and processing the resource utilization data to generate a resource optimization suggestion report for the corresponding production system includes: Analyzing the resource utilization data of the cloud hosts of each production system using a rule-based resource optimization analysis method or a trend prediction-based resource optimization analysis method; Determine bottlenecks and excess points in resource utilization of each production system based on the analysis results; A resource optimization suggestion report for the corresponding production system is generated based on the bottleneck points and the excess points. The resource optimization suggestion report includes: resource analysis results and resource optimization suggestion measures.

4. The cloud resource configuration optimization method according to claim 1, characterized in that: Generating a resource configuration optimization work order based on the resource optimization suggestion report includes: Generate a corresponding resource configuration optimization work order based on the resource analysis results and resource optimization recommended measures in the resource optimization recommendation report. The resource configuration optimization work order includes: setting up a standardized resource optimization process. The resource optimization process includes: simulation environment construction, simulation detection execution, cloud orchestration template preparation, system and environment backup, resource configuration change, system switching operation, optimization effect verification and system baseline update; The resource configuration optimization work orders are managed and tracked to ensure that the resource optimization suggestions can be effectively implemented.

5. The cloud resource configuration optimization method according to claim 1, characterized in that: The step of optimizing the work order for resource configuration, building a simulation test environment for the corresponding production system, and using simulation data to perform simulation testing on the operation of the built production system includes: According to the resource configuration optimization work order, the production system corresponding to the cloud host is issued in batches, and a simulation environment identical to the optimized production environment is built; Using the detection tools of each production system to generate simulation data, simulate the actual operation of the production system, and perform simulation performance testing; Using the production system standard cases in the test case library in the simulation environment, testing the production system in the simulation environment, and monitoring the performance and stability of the production system; Based on the test results, a simulation test report is generated, wherein the simulation test report includes: performance and risk assessment, and feasibility results of resource optimization.

6. The cloud resource configuration optimization method according to claim 1, characterized in that: After the simulation test passes, a cloud orchestration template is generated based on the simulation test environment and automatically deployed to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources, including: After the simulation test passes, an optimized resource configuration declaration file is generated based on the simulation test environment using the infrastructure as code technology and the Terraform template. The resource configuration declaration file includes: all cloud resources used by the production system; Use the Terraform template to create a resource-optimized execution plan; Running the execution plan will apply complex resource changes to the cloud host of the cloud platform corresponding to the production system, and perform resource optimization operations on the cloud resources.

7. The cloud resource configuration optimization method according to claim 6, characterized in that: After the simulation test passes, a cloud orchestration template is generated based on the simulation test environment, and the template is automatically deployed to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources, and further includes: Before running the execution plan, back up the resources, programs and data of the cloud environment; During the resource optimization operation of the execution plan, if an exception occurs, the configuration before the resource optimization is automatically rolled back to maintain stable operation of the system.

8. A cloud resource configuration optimization device, characterized in that: include: The acquisition module is used to obtain the resource utilization data of the cloud host of each production system in the rail transit system; A processing module, used to analyze and process the resource utilization data and generate a resource optimization recommendation report for the corresponding production system; A generation module, used to generate a resource configuration optimization work order based on the resource optimization suggestion report; A simulation module is used to optimize the work order according to the resource configuration, build a simulation test environment for the corresponding production system, and use simulation data to simulate the operation of the built production system; A cloud orchestration module is used to generate a cloud orchestration template based on the simulation test environment after the simulation test passes, and automatically deploy it to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources; The recording module is used to record the cloud resource configuration of the production system after resource optimization as a resource configuration baseline, so as to compare the resource usage of each production system.

9. A cloud resource configuration optimization system, characterized in that: include: The monitoring agent module is used to collect resource utilization data of the cloud hosts of various production systems in the rail transit system in real time; A data access service module is used to verify the validity of the resource utilization data and perform preprocessing to ensure the accuracy and completeness of the resource utilization data; And store the pre-processed resource utilization data into the database; A cloud resource monitoring module, used to obtain resource utilization data of cloud hosts of each production system from the database; An intelligent analysis module, used to analyze and process the real-time resource utilization data and historical resource utilization data obtained by the cloud resource monitoring module, and generate a resource optimization recommendation report for the corresponding production system; A work order management module, used to generate a resource configuration optimization work order based on the resource optimization suggestion report; A simulation detection module is used to optimize the work order according to the resource configuration, build a simulation test environment for the corresponding production system, and use simulation data to simulate the operation of the built production system; A cloud orchestration module is used to generate a cloud orchestration template based on the simulation test environment after the simulation test passes, and automatically deploy it to the cloud host of the corresponding production system to perform resource optimization operations on cloud resources; The baseline module is used to record the cloud resource configuration after resource optimization of the production system as a resource configuration baseline, so as to compare the resource usage of each production system.

10. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the cloud resource configuration optimization method according to any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the cloud resource configuration optimization method as described in any one of claims 1 to 7.

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