Cloud service simulation method and device, computer equipment, medium and product

Abstract

The invention relates to a cloud service simulation method and device, computer equipment, a medium and a product. Comprising the steps of generating a to-be-simulated cloud service according to request type information carried by a cloud service simulation request; target overall simulation data of the to-be-simulated cloud service is output through a preset data evaluation model, and the preset data evaluation model is obtained based on overall simulation data iteration training of the full cloud service; and simulating the to-be-simulated cloud service according to the target overall simulation data. By adopting the method, the simulation efficiency and the simulation accuracy of cloud service simulation are both considered.

Description

Technical Field

[0001] The present application relates to the technical field of cloud services, and in particular, to a cloud service simulation method, device, computer device, computer-readable storage medium, and computer program product. Background Art

[0002] Cloud services refer to various cloud services provided based on the Internet, such as cloud backup services, cloud computing services, and cloud storage services, etc. The implementation of cloud services requires the support of cloud resources such as the cloud and the cloud base. To save resource investment, cloud service simulation is usually carried out through POC (Proof of Concept) to achieve the purpose of users' understanding, trial, and verification of the cloud service platform.

[0003] Currently, in the process of cloud service simulation, it is usually carried out separately for multiple modules of the cloud management or the cloud base according to the actual needs of customers, and then final data integration is carried out. However, due to the large number of modules and middleware involved in the cloud management and the cloud base, and the complex interweaving of links, the overall simulation period is relatively long. At the same time, due to the data consistency problem between multiple modules, there are differences between the simulated data calculated under multiple modules and the real simulated data, resulting in the actual simulation results not matching the expectations. Therefore, currently, it is impossible to balance the simulation efficiency and simulation accuracy of cloud service simulation. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a cloud service simulation method, device, computer device, computer-readable storage medium, and computer program product that cannot balance the simulation efficiency and simulation accuracy of cloud service simulation.

[0005] In a first aspect, the present application provides a cloud service simulation method, including:

[0006] Generating a cloud service to be simulated according to the request type information carried in the cloud service simulation request;

[0007] Outputting the target overall simulation data of the cloud service to be simulated through a preset data evaluation model, where the preset data evaluation model is iteratively trained based on the overall simulation data of all cloud services;

[0008] Simulating the cloud service to be simulated according to the target overall simulation data.

[0009] In one embodiment, the cloud service to be simulated includes an interactive cloud service to be simulated; the simulating the cloud service to be simulated according to the target overall simulation data includes:

[0010] Obtaining the target performance evaluation data of the interactive cloud service to be simulated;

[0011] When the performance evaluation data meets the preset evaluation conditions, simulate the to-be-simulated interactive cloud service according to the target overall simulation data and the target performance evaluation data.

[0012] In one embodiment, after simulating the to-be-simulated cloud service according to the target overall simulation data, the method further includes:

[0013] Split the target overall simulation data to obtain local simulation data corresponding to each cloud service link of the to-be-simulated cloud service;

[0014] Generate multiple simulation performance data of the to-be-simulated cloud service according to all the local simulation data;

[0015] Store the multiple simulation performance data in a preset storage space.

[0016] In one embodiment, the storing the simulation performance data in a preset storage space includes:

[0017] Determine the performance evaluation index of each simulation performance data;

[0018] Obtain the performance evaluation data of the to-be-simulated cloud service by fusing multiple performance evaluation indexes;

[0019] Store the multiple simulation performance data and the performance evaluation data together in a preset storage space.

[0020] In one embodiment, before outputting the target overall simulation data of the to-be-simulated cloud service through a preset data evaluation model, the method further includes:

[0021] Collect cloud service initial data in a preset data collection mode;

[0022] Process the cloud service initial data to obtain cloud service basic data;

[0023] Generate the overall simulation data of each cloud service according to the cloud service basic data.

[0024] In one embodiment, the processing the cloud service initial data to obtain cloud service basic data includes:

[0025] Perform desensitization processing on the cloud service initial data to obtain cloud service desensitized data;

[0026] Perform duplicate removal processing on the cloud service desensitized data to obtain cloud service duplicate-removed data;

[0027] Perform noise reduction processing on the deduplicated data of the cloud service to obtain the basic data of the cloud service.

[0028] In a second aspect, the present application also provides a cloud service simulation method device, including:

[0029] A generation module, configured to generate a cloud service to be simulated according to the request type information carried in the cloud service simulation request;

[0030] An output module, configured to output the target overall simulation data of the cloud service to be simulated through a preset data evaluation model, where the preset data evaluation model is iteratively trained based on the overall simulation data of all cloud services;

[0031] A simulation module, configured to simulate the cloud service to be simulated according to the target overall simulation data.

[0032] In one embodiment, the cloud service to be simulated includes an interactive cloud service to be simulated; the simulation module is further configured to:

[0033] Obtain the target performance evaluation data of the interactive cloud service to be simulated;

[0034] When the performance evaluation data meets the preset evaluation conditions, simulate the interactive cloud service to be simulated according to the target overall simulation data and the target performance evaluation data.

[0035] In one embodiment, the device is further configured to:

[0036] Split the target overall simulation data to obtain local simulation data corresponding to each cloud service link of the cloud service to be simulated;

[0037] Generate multiple simulation performance data of the cloud service to be simulated according to all local simulation data;

[0038] Store the multiple simulation performance data in a preset storage space.

[0039] In one embodiment, the device is further configured to:

[0040] Split the target overall simulation data to obtain local simulation data corresponding to each cloud service link of the cloud service to be simulated;

[0041] Generate multiple simulation performance data of the cloud service to be simulated according to all local simulation data;

[0042] Store the multiple simulation performance data in a preset storage space.

[0043] In one embodiment, the device is further configured to:

[0044] Determine the performance evaluation index for each of the said simulation performance data;

[0045] By fusing multiple performance evaluation indexes, obtain the performance evaluation data of the to-be-simulated cloud service;

[0046] Store the multiple simulation performance data and the performance evaluation data together in a preset storage space.

[0047] In one embodiment, the device is further configured to:

[0048] Collect the initial data of the cloud service in a preset data collection mode;

[0049] Process the initial data of the cloud service to obtain the basic data of the cloud service;

[0050] Generate the overall simulation data of each cloud service according to the basic data of the cloud service.

[0051] In one embodiment, the device is further configured to:

[0052] Perform desensitization processing on the initial data of the cloud service to obtain the desensitized data of the cloud service;

[0053] Perform duplicate removal processing on the desensitized data of the cloud service to obtain the deduplicated data of the cloud service;

[0054] Perform noise reduction processing on the deduplicated data of the cloud service to obtain the basic data of the cloud service.

[0055] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0056] Generate a to-be-simulated cloud service according to the request type information carried in the cloud service simulation request; output the target overall simulation data of the to-be-simulated cloud service through a preset data evaluation model, where the preset data evaluation model is iteratively trained based on the overall simulation data of the full-scale cloud service; simulate the to-be-simulated cloud service according to the target overall simulation data.

[0057] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0058] Generate a cloud service to be simulated based on the request type information carried in the cloud service simulation request; output the target overall simulation data of the cloud service to be simulated through a preset data evaluation model, wherein the preset data evaluation model is iteratively trained based on the overall simulation data of all cloud services; simulate the cloud service to be simulated based on the target overall simulation data.

[0059] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0060] Generate a cloud service to be simulated based on the request type information carried in the cloud service simulation request; output the target overall simulation data of the cloud service to be simulated through a preset data evaluation model, wherein the preset data evaluation model is iteratively trained based on the overall simulation data of all cloud services; simulate the cloud service to be simulated based on the target overall simulation data.

[0061] The cloud service simulation method, apparatus, computer equipment, computer-readable storage medium and computer program product described above first generate a cloud service to be simulated based on the request type information carried by the cloud service request simulation, so that in the face of different cloud service requests, corresponding cloud services to be simulated can be generated in a targeted manner, and then the target overall simulation data of the cloud service to be simulated is output through a preset data evaluation model, so as to achieve the purpose of matching the complete target overall simulation data for the cloud service to be simulated in a targeted manner, and finally rely on the target overall simulation data to simulate the cloud service to be simulated. Since the cloud service to be simulated is implemented based on the complete target overall simulation data, the link interweaving problem in the cloud service simulation process is avoided by completely simulating the cloud service. At the same time, since the preset data evaluation model is obtained by iterative training based on the overall simulation data of all cloud services, the preset data evaluation model has the ability to accurately output the overall simulation data for any cloud service that needs to be simulated. Therefore, the target overall simulation data can also simultaneously ensure consistency with the real simulation data, thereby overcoming the long overall simulation cycle caused by the large number of modules and middleware involved in the cloud management and cloud base, and the complex links. At the same time, due to the data consistency problem between multiple modules, there is a difference between the simulation data calculated under multiple modules and the real simulation data, which makes the actual simulation results inconsistent with expectations. Technical defects, therefore, take into account both the simulation efficiency and simulation accuracy of cloud service simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] To more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required for the description of the embodiments or the related art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0063] Figure 1 It is a schematic flowchart of the cloud service simulation method in an embodiment;

[0064] Figure 2 It is a partial simulation flowchart of the first type of cloud service to be simulated in the cloud service simulation method in an embodiment;

[0065] Figure 3 It is a schematic flowchart of the cloud service simulation method in another embodiment;

[0066] Figure 4 It is a partial simulation flowchart of the second type of cloud service to be simulated in the cloud service simulation method in another embodiment;

[0067] Figure 5 It is a schematic diagram of the modules of the cloud service simulation system in the cloud service simulation method in another embodiment;

[0068] Figure 6 It is a structural block diagram of the cloud service simulation device;

[0069] Figure 7 It is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0070] In order to make the purpose, technical solutions and advantages of the present application clearer, the following further details the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0071] First of all, it should be understood that the main types of services provided by cloud services are public cloud, private cloud, hybrid cloud, etc. Different service types have their own characteristics. Among them, the public cloud is operated by a third party, has a wide customer base, strong scalability, and high flexibility; the private cloud is self-operated by an organization or enterprise, mainly provides services internally, has strong business pertinence and higher security; the hybrid cloud combines the advantages of the private cloud and the public cloud, and provides more flexible cloud services on the premise of ensuring security; the cloud resources supporting cloud services mainly include cloud management and cloud infrastructure. Among them, cloud management is a cloud management platform for managing public cloud, private cloud or hybrid cloud, including functions such as multi-cloud resource management, metering and billing, monitoring and alerting, and third-party integration, which provides convenience for enterprises or organizations to uniformly manage and schedule IT resources, and can greatly improve the efficiency of their digital transformation and operation and maintenance management. The cloud infrastructure is the infrastructure of cloud computing, providing hardware and software support for upper-layer applications, including computing devices, storage devices, network devices, and a series of devices and technologies such as the operating system, virtualization, automation, and security running on them. Flexible resource management, security and stability, and fault-tolerant continuous operation are its core capabilities. And POC (Proof of Concept) generally refers to proof of concept, that is, a process, a process at the initial stage of a project to prove whether the project is feasible through a series of materials such as cases and evidence. The groups it faces are mainly investors, customers, and relevant professionals, to prove the feasibility of the project to them, increase their trust, and provide a solid foundation for the implementation of the project. In the professional field of cloud computing, POC is mainly to build a cloud service platform (including cloud management and cloud infrastructure, etc.) for customers with the smallest resource investment under the condition of meeting customer needs and providing corresponding solutions, so as to achieve the purpose of users' understanding, trial use, and verification of the cloud service platform. It can be understood that with the increasingly fierce competition in the cloud service market, POC plays a crucial role in the market competition. However, even in the POC environment, as the two core modules for cloud computing to provide services externally, cloud management and cloud infrastructure also face many new challenges in helping enterprises or organizations with digital transformation and upgrading in the face of various market, business, and customer needs. Specifically, due to the large number of modules and middleware involved in cloud management and cloud infrastructure, and the complex intertwining of links, the overall simulation cycle is relatively long. At the same time, due to the data consistency problem among multiple modules, there is a difference between the simulated data calculated under multiple modules and the real simulated data, resulting in the actual simulation results not matching the expectations. In addition, in the situation of increasingly fierce competition in the cloud service market, enterprises or organizations have more and more requirements for POC during project bidding, and some business scenarios have more stringent requirements for the performance and stability of cloud services. Customizing the POC environment also poses difficulties in terms of cost for cloud service providers, such as long cycle, large investment in manpower, and large facility resources. Therefore, there is an urgent need for a cloud service simulation method that takes into account both the simulation efficiency and simulation accuracy of cloud service simulation.

[0072] In one embodiment, Figure 1 As shown, a cloud service simulation method is provided. This embodiment takes the method applied to a terminal as an example. The terminal is provided with a cloud service simulation system. The terminal includes but is not limited to a personal computer, a laptop computer, a smart phone and a tablet computer. The cloud service simulation system includes a generation module, an output module and a simulation module. The generation module is used to generate a cloud service to be simulated according to the request type information carried by the cloud service simulation request. The output module is used to output the target overall simulation data of the cloud service to be simulated through a preset data evaluation model. The preset data evaluation model is obtained by iterative training based on the overall simulation data of the full amount of cloud services. The simulation module is used to simulate the cloud service to be simulated according to the target overall simulation data. Through the information interaction between the generation module, the output module and the simulation module, the target overall simulation data output by the preset data evaluation model is relied on. The simulation of the cloud service to be simulated generated for a specific cloud service type, since the cloud service to be simulated is implemented based on the complete target overall simulation data, and then by completely simulating the cloud service, the link interweaving problem in the cloud service simulation process is avoided. At the same time, since the preset data evaluation model is obtained by iterative training based on the overall simulation data of the full amount of cloud services, the preset data evaluation model has the ability to accurately output the overall simulation data for any cloud service that needs to be simulated. Therefore, the target overall simulation data can also be synchronously ensured to be consistent with the real simulation data. Therefore, the technical effect of taking into account both the simulation efficiency and the simulation accuracy of the cloud service simulation is achieved. It can be understood that this method can also be applied to servers, and can also be applied to systems including terminals and servers, and is implemented through the interaction between terminals and servers. In this embodiment, the method includes the following steps 202 to 206. Among them:

[0073] Step 202: Generate a cloud service to be simulated according to the request type information carried in the cloud service simulation request.

[0074] It should be noted that the request type information is used to characterize the request type of the cloud service simulation request. The cloud service simulation request can be obtained through user input or automatically generated. It can be understood that the cloud service simulation system is deployed with a docking module. The docking module has the ability to identify the request type of the cloud service simulation request and generate cloud services to be simulated in a targeted manner. Among them, the cloud service to be simulated refers to the cloud service waiting to be simulated, which can specifically be a request service, a data storage service, a computing service, and a network service. For example, in an implementable manner, assuming that the request type information is a docking parameter, refer to Figure 2 , Figure 2To represent a partial simulation flow chart for simulating a first type of cloud service to be simulated, the docking module serves as the overall entrance and exit for the entire cloud service simulation system to provide simulation services and evaluation reports externally. It determines the functional services provided by the entire system based on the docking parameters with the outside world. Specifically, it can provide a simulated complete cloud service link or simulate user interactions and requests externally. When the docking parameter is the URL-IN address requested from the outside, it is recognized that the system needs to simulate a complete cloud service link, and the cloud service simulation system will return the simulated response through the docking module. Among them, the first type of cloud service to be simulated can be understood as a cloud service simulated by the cloud service simulation system in the absence of a cloud base.

[0075] As an example, step 202 includes: extracting request type information from the cloud service simulation request, querying the type of cloud service requested by the cloud service simulation request based on the request type information, and generating the cloud service to be simulated corresponding to the cloud service type.

[0076] In step 204, the target overall simulation data of the cloud service to be simulated is output through a preset data evaluation model, where the preset data evaluation model is obtained by iteratively training based on the overall simulation data of the full-scale cloud service.

[0077] It should be noted that the cloud service simulation system can be synchronously deployed with a model module. The model module is provided with a preset data evaluation model, which is iteratively trained based on the overall simulation data of the full-scale cloud service. The preset data evaluation model is used to output the target overall mode data adapted to the cloud service to be simulated. It can be understood that the preset data evaluation model can be a single model or composed of multiple models combined. The preset data evaluation model takes the request issued by the cloud management as the input IN and the target overall simulation data as the output OUT. Among them, the overall simulation data is used to represent the resource data required to simulate the complete cloud service, specifically, it can be the complete cloud service processing link. The full-scale cloud service refers to all the cloud services that the cloud service simulation system can provide. By training appropriate model strategies and model parameters through the overall simulation data of the full-scale cloud service, the preset data evaluation model can be enabled to completely simulate the processing link of the cloud service and respond to any request issued by the cloud management. For example, in an implementable manner, the model module can also randomly select one or more requests or even all requests from the data set SET of the stored operation and management requests to simulate the interaction operations and requests issued by the cloud management user to the cloud management, and store the response time, various response data of success or failure obtained from the requests in the storage module. It can be understood that after completing the simulation of a certain cloud service, the cloud service log data after all requests issued by the cloud management can be collected through the acquisition module deployed in the cloud service simulation system, and then the iterative training of the preset data evaluation model can be carried out. As the data in the storage module continues to accumulate, the model module can dynamically adjust the model parameters or model strategies of the preset data evaluation model by using algorithms similar to the unsupervised learning K-Means algorithm, etc., to make it more suitable for the real scenario and the prediction and simulation more accurate.

[0078] As an example, step 204 includes: inputting the cloud service simulation request into the preset data evaluation model, and outputting the target overall simulation data of the cloud service to be simulated through the preset data evaluation model, where the preset data evaluation model is iteratively trained based on the overall simulation data of the full-scale cloud service.

[0079] Step 206, simulate the cloud service to be simulated according to the target overall simulation data.

[0080] It should be noted that by completing the simulation of the cloud service to be simulated through the target overall simulation data, the complete simulation of the cloud service can be realized, which not only provides a very convenient solution for customers to evaluate the overall performance and stability of the cloud service, but also can save huge human and facility costs for cloud service providers, greatly improving the efficiency. That is, simulating the cloud service to be simulated with the target overall simulation data can balance the simulation efficiency and simulation accuracy of the cloud service simulation.

[0081] As an example, step 206 includes: simulating the cloud service to be simulated through the target overall simulation data.

[0082] In the above-mentioned cloud service simulation method, the request type information carried by the cloud service request simulation is first used to generate the cloud service to be simulated, so that in the face of different cloud service requests, the corresponding cloud service to be simulated can be generated in a targeted manner, and then the target overall simulation data of the cloud service to be simulated is output through the preset data evaluation model, so as to achieve the purpose of matching the complete target overall simulation data for the cloud service to be simulated in a targeted manner, and finally rely on the target overall simulation data to simulate the cloud service to be simulated. Since the cloud service to be simulated is implemented based on the complete target overall simulation data, the link interweaving problem in the cloud service simulation process is avoided by completely simulating the cloud service. At the same time, due to the preset data The evaluation model is obtained through iterative training based on the overall simulation data of all cloud services, so that the preset data evaluation model has the ability to accurately output the overall simulation data for any cloud service that needs to be simulated. Therefore, the target overall simulation data can also simultaneously ensure consistency with the real simulation data, thus overcoming the long overall simulation cycle caused by the large number of modules and middleware involved in the cloud management and cloud base, and the complex links. At the same time, due to the data consistency problem between multiple modules, there are differences between the simulation data calculated under multiple modules and the real simulation data, which makes the actual simulation results inconsistent with expectations. Therefore, both the simulation efficiency and accuracy of cloud service simulation are taken into account.

[0083] In one embodiment, Figure 3 As shown, the cloud service to be simulated includes the interactive cloud service to be simulated; according to the target overall simulation data, the cloud service to be simulated is simulated, including:

[0084] Step 302: Obtain target performance evaluation data of the interactive cloud service to be simulated.

[0085] It should be noted that the interactive cloud service to be simulated refers to a cloud service that simulates the user's interaction and request to the cloud on the basis of the non-cloud management service, that is, the second type of cloud service to be simulated. For example, in an implementable manner, assuming that the request type information is a docking parameter, refer to Figure 4 , Figure 4To represent the partial simulation flowchart of the second type of cloud service to be simulated, that is, when the docking parameter is the URL-OUT address of the externally received request, the docking module recognizes that the cloud service simulation system needs to simulate the user's interaction and request. The cloud service simulation system will forward the simulated request to the corresponding address through the docking module, and the docking module will synchronously transfer the response data of the request to the model module. Further, when the docking parameter is URL-OUT and REPORT is TRUE, after simulating the issued cloud management request, the performance and stability evaluation report generated by the analysis device will also be provided through the docking module.

[0086] It should be noted that the cloud service simulation system can also synchronously deploy an analysis module, so that while completing the cloud service simulation, a performance evaluation report can be issued based on the target performance evaluation data. Therefore, when simulating the second type of cloud service to be simulated, synchronously providing a report through the analysis module mainly serves to analyze and compare the data related to the stability and performance of different cloud bases to assist in the final decision-making. Among them, the target performance evaluation data is used to evaluate the stability of the cloud service to be simulated.

[0087] As an example, step 302 includes: obtaining the target performance evaluation data of the cloud service of the interaction to be simulated.

[0088] Step 304, when the performance evaluation data meets the preset evaluation conditions, simulate the cloud service of the interaction to be simulated according to the target overall simulation data and the target performance evaluation data.

[0089] It should be noted that the preset evaluation conditions are used to evaluate whether an evaluation report can be issued. For example, the preset evaluation conditions can specifically be that REPORT is TRUE. When the preset evaluation conditions are met, the analysis module will send the target performance evaluation data to the docking module, and the docking module will cooperate with the target overall simulation data to simulate the cloud service of the interaction to be simulated.

[0090] As an example, step 304 includes: when it is detected that the performance evaluation data meets the preset evaluation conditions, jointly simulate the cloud service of the interaction to be simulated through the target overall simulation data and the target performance evaluation data.

[0091] In this embodiment, during the simulation of the to-be-simulated interactive cloud service, different data is used to complete the simulation of the to-be-simulated interactive cloud service based on the different types of the to-be-simulated interactive cloud service. That is, when the to-be-simulated cloud service is the to-be-simulated interactive cloud service, first, the target performance evaluation data of the to-be-simulated interactive cloud service is obtained. Then, when the performance evaluation data meets the preset evaluation conditions, relying on the target performance evaluation data and the target overall simulation data, the simulation of the to-be-simulated interactive cloud service is realized. Thus, while taking into account the simulation accuracy and simulation efficiency of the cloud service simulation, the simulation flexibility of the cloud service simulation is improved.

[0092] In one embodiment, after simulating the to-be-simulated cloud service according to the target overall simulation data, the method further includes:

[0093] The target overall simulation data is split to obtain the local simulation data corresponding to each cloud service link of the to-be-simulated cloud service; according to all the local simulation data, multiple simulation performance data of the to-be-simulated cloud service are generated; and the multiple simulation performance data are stored in a preset storage space.

[0094] It should be noted that to ensure the accurate evaluation of the stability of the cloud service simulation, the analysis module deployed in the cloud service simulation system can be used to analyze and process the target overall simulation data, and the storage module deployed in the cloud service simulation system can be used to store the corresponding simulation performance data for subsequent obtaining of the performance evaluation data. Among them, the simulation performance data refers to the performance data obtained through simulating the cloud service interaction, and specifically can be the complete processing time of each request, the failure rate, the throughput per unit time, and the resource consumption rate, etc. For example, in an implementable manner, the analysis module can split, classify, and perform AND operations on the response data and the corresponding log data obtained after simulating the cloud management interaction and issuing the request according to the stored correspondence between the request and the complete service link to calculate the complete processing time, failure rate, throughput per unit time, and resource consumption rate, etc. of each request.

[0095] As an example, the target overall simulation data is split into the local simulation data corresponding to each cloud service link of the to-be-simulated cloud service, where the cloud service link can specifically be a certain request in the to-be-simulated cloud service; by calculating all the local simulation data, multiple simulation performance data of the to-be-simulated cloud service are obtained; and the multiple simulation performance data are stored in a preset storage space.

[0096] In this embodiment, after the simulation of the cloud service to be simulated is completed, by splitting the target overall simulation data, local simulation data corresponding to multiple cloud service links of the cloud service to be simulated is obtained. Then, by calculating all the local simulation data, multiple simulation performance data of the cloud service to be simulated is obtained. Finally, by storing the multiple simulation performance data in a preset storage space, a foundation can be laid for simulating the interactive cloud service to be simulated. Therefore, a foundation is laid for improving the simulation flexibility of cloud service simulation.

[0097] In one embodiment, storing the simulation performance data in a preset storage space includes:

[0098] Determine the performance evaluation index of each simulation performance data; obtain the performance evaluation data of the cloud service to be simulated by fusing multiple performance evaluation indexes; store the multiple simulation performance data and the performance evaluation data together in the preset storage space.

[0099] It should be noted that the performance evaluation index is used to evaluate the characteristics of the simulation performance data. Specifically, it can be the degree of dispersion of the simulation performance data. For example, in an implementable manner, the performance evaluation index is calculated through the following index calculation formula, and the index calculation formula is specifically as follows:

[0100]

[0101] Wherein, represents the degree of dispersion of the simulation performance data, represents the standard deviation of the simulation performance data, is the average value of the simulation performance data. It can be understood that, the size of can be used as a representative of the stability of the cloud service corresponding to the current request. The larger it is, the weaker the stability; The smaller it is, the stronger the stability. Further, the analysis module will statistically summarize the simulation performance data and the performance evaluation data of each request to generate a performance and stability evaluation report of the cloud service.

[0102] As an example, each piece of simulated performance data is input into the metric calculation formula, and the performance evaluation metric for each piece of simulated performance data is calculated through the metric calculation formula; by fusing multiple performance evaluation metrics, the performance evaluation data of the cloud service to be simulated is obtained; the multiple pieces of simulated performance data and the performance evaluation data are respectively stored in different storage areas of a preset storage space. In this embodiment, the performance evaluation data of the cloud service to be simulated is calculated through the performance evaluation metric of each piece of simulated performance data, and then the simulated performance data and the performance evaluation data are respectively stored in different storage areas of the preset storage space, thereby ensuring that when a cloud service simulation request is received, the simulation of the cloud service to be interacted can be accurately and efficiently completed. Therefore, it further lays a foundation for improving the simulation flexibility of cloud service simulation.

[0103] In one embodiment, before outputting the target overall simulation data of the cloud service to be simulated through the preset data evaluation model, the method further includes:

[0104] Collect the initial cloud service data in the preset data collection mode; process the initial cloud service data to obtain the basic cloud service data; generate the overall simulation data of each cloud service according to the basic cloud service data.

[0105] It should be noted that before simulating the cloud service, the initial cloud service data of the full-scale cloud service can be collected by deploying a collection module in the cloud service simulation system, and then the overall simulation data can be obtained through processing, so as to provide a data set for the training of the preset data evaluation model. The preset data collection mode is used to represent the method of collecting the overall simulation data. For example, in an implementable manner, three different data collection modes can be preset. Among them, automatic collection is marked as data collection mode 1, manual entry is marked as collection mode 0, and the automatic and manual hybrid entry mode is marked as data collection mode 2. The data source of the initial cloud service data can mainly be the log records of each service module in each resource pool of the live network. In the case of mode 1, the log can be pulled from the specified log server during the low-traffic period by setting a scheduled task. In the case of mode 0, the log file can be entered manually or imported. In the case of mode 2, both automatic collection and manual entry are supported. The method of converting the basic cloud service data into the overall simulation data of each cloud service can be to split and reorganize the complete link logs of the cloud service corresponding to each request through distributed link tracing tools such as Zipkin, generate the complete set of cloud management requests and their corresponding complete link processing of the cloud service, and classify and store them in the storage module, so as to obtain the overall simulation data of the full-scale cloud service for training the preset data evaluation model.

[0106] As an example, the initial cloud service data is collected through the manual data collection mode and the automatic data collection mode; the basic cloud service data is obtained by processing the initial cloud service data; and the overall simulation data of each cloud service is converted from the basic cloud service data through the Zipkin tool.

[0107] In this embodiment, before simulating the cloud service, first, the initial cloud service data is collected based on the preset data collection mode. Then, the basic cloud service data is obtained by processing the initial cloud service data. Finally, with the help of the data conversion tool, the collection of the overall simulation data of each cloud service from the basic cloud service data is completed, thus achieving the purpose of preparing the overall simulation data of the full-scale cloud service for training for the preset data evaluation model. Therefore, it further lays a foundation for balancing the simulation efficiency and simulation accuracy of the cloud service simulation.

[0108] In one embodiment, processing the initial cloud service data to obtain the basic cloud service data includes:

[0109] Performing desensitization processing on the initial cloud service data to obtain desensitized cloud service data; performing deduplication processing on the desensitized cloud service data to obtain deduplicated cloud service data; and performing noise reduction processing on the deduplicated cloud service data to obtain the basic cloud service data.

[0110] It should be noted that to avoid the privacy and security risks caused by the leakage of customer data, there may be sensitive data in the initial cloud service data. Based on the characteristics of the data fields that may pose security risks in the cloud service platform logs, such as resource names, remarks, and customer login accounts, names, and email addresses, the preprocessing device uses data perturbation or desensitization technologies such as regular expression matching, mask covering, desensitization plugins, and tools to perform secondary desensitization processing on the collected or input log data and then transfer it to the data processing device for processing; further, for the desensitized cloud service data obtained after desensitization processing, deduplication processing needs to be performed again. Specifically, the processed log data can be deduplicated through the Bloom filter in the processing module of the cloud service simulation system; further, for the deduplicated cloud service data obtained after deduplication processing, noise reduction processing needs to be performed again to obtain the basic cloud service data that can be used to generate the overall simulation data. Specifically, noise reduction can be performed through mean filling and the Z-score method. The relevant formula for mean filling is as follows:

[0111]

[0112] Where, is the missing data value for filling, n is the total number of observations in the dataset, m is the number of missing data values, is the i-th observation value; the calculation formula of the Z-score method is as follows:

[0113]

[0114] where x is the value of the data point, is the average value of the data set, is the standard deviation of the data set. It can be understood that values where z > 3 or z < -3 are outliers.

[0115] As an example, the initial data of the cloud service is desensitized through a regular matching tool to obtain desensitized cloud service data; the desensitized cloud service data is deduplicated through a Bloom filter to obtain deduplicated cloud service data; the deduplicated cloud service data is denoised through a mean filling method to obtain basic cloud service data.

[0116] In this embodiment, through multiple data methods such as desensitization processing, deduplication processing, and denoising processing, the initial data of the cloud service is processed to obtain basic cloud service data, thereby ensuring that the basic cloud service data used to generate the overall simulated data meets the data requirement standards, and thus laying a foundation for maximizing both the simulation efficiency and simulation accuracy of the cloud service simulation.

[0117] In an implementable manner, referring to Figure 5 , Figure 5 is a schematic diagram of the modules of the cloud service simulation system. Among them, the cloud service simulation system may specifically include an acquisition module, a preprocessing module, a processing module, a storage module, a model module, an analysis module, and a docking module. Among them, the docking module can either simulate the cloud service and complete the cloud service response based on the data interaction with the model module, or simulate an interaction request and output the interaction request based on the data interaction with the model module and the storage module. It can also output a simulation stability assessment report via the analysis module while completing the simulation of the cloud service to be simulated. The model module is mainly used to provide the target overall simulation data of the cloud service to be simulated, the storage module is used to store the overall simulation data of the full amount of cloud services, the processing module is used to deduplicate and denoise the data, the acquisition module is used to collect the data, and the preprocessing module is used to desensitize the data. It can be understood that the dotted line represents the simulation process of the first type of cloud service to be simulated, and the solid line represents the simulation process of the second type of cloud service to be simulated.

[0118] It can be understood that based on the cloud service simulation process provided in this embodiment, the following technical effects can be achieved: 1) Using the desensitized log data of the existing network resource pool as the training data of the model, the quality, scale, representativeness, integrity, and security of the data are reliably guaranteed, and the preset data evaluation model trained will also be more matching and accurate in simulation and prediction; 2) This embodiment can not only simulate the complete data processing link of the cloud service, but also completely simulate the operations, interactions, and requests of users on the cloud management platform throughout the entire project cycle. Its coverage is wider and more comprehensive. Especially in the POC scenario, it has many benefits for both cloud service users and providers; 3) After docking with different cloud service providers, the performance and stability evaluation data of the cloud service can be compared horizontally and vertically, which is convenient for clearly understanding the advantages and disadvantages of the cloud service provider itself and the improvement rate before and after iteration; 4) This embodiment uses large model and AI application technologies without excessive human intervention. With the accumulation of data resources, the training and optimization of its model can be self-closed.

[0119] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0120] Based on the same inventive concept, an embodiment of the present application also provides a cloud service simulation device for implementing the above-mentioned cloud service simulation method. The solution provided by this device to solve problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the cloud service simulation device provided below can refer to the limitations on the cloud service simulation method in the above text, and will not be repeated here.

[0121] In an exemplary embodiment, as Figure 6 shown, a cloud service simulation device is provided, including: a generation module 401, an output module 402, and a simulation module 403, where:

[0122] The generation module 401 is configured to generate a cloud service to be simulated according to the request type information carried in the cloud service simulation request;

[0123] An output module 402 is configured to output target overall simulation data of the to-be-simulated cloud service through a preset data evaluation model, where the preset data evaluation model is iteratively trained based on overall simulation data of all cloud services;

[0124] A simulation module 403 is configured to simulate the to-be-simulated cloud service according to the target overall simulation data.

[0125] In one embodiment, the to-be-simulated cloud service includes a to-be-simulated interactive cloud service; the simulation module 403 is further configured to:

[0126] Obtain target performance evaluation data of the to-be-simulated interactive cloud service; and simulate the to-be-simulated interactive cloud service according to the target overall simulation data and the target performance evaluation data when the performance evaluation data meets a preset evaluation condition.

[0127] In one embodiment, the apparatus is further configured to:

[0128] Split the target overall simulation data to obtain local simulation data corresponding to respective cloud service links of the to-be-simulated cloud service; generate multiple simulation performance data of the to-be-simulated cloud service according to all local simulation data; and store the multiple simulation performance data in a preset storage space.

[0129] In one embodiment, the apparatus is further configured to:

[0130] Determine a performance evaluation index for each simulation performance data; obtain performance evaluation data of the to-be-simulated cloud service by fusing multiple performance evaluation indexes; and jointly store the multiple simulation performance data and the performance evaluation data in a preset storage space.

[0131] In one embodiment, the apparatus is further configured to:

[0132] Collect initial cloud service data in a preset data collection mode; process the initial cloud service data to obtain basic cloud service data; and generate overall simulation data for each cloud service according to the basic cloud service data.

[0133] In one embodiment, the apparatus is further configured to:

[0134] Perform desensitization processing on the initial cloud service data to obtain desensitized cloud service data; perform deduplication processing on the desensitized cloud service data to obtain deduplicated cloud service data; and perform noise reduction processing on the deduplicated cloud service data to obtain the basic cloud service data.

[0135] Each module in the above cloud service simulation device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0136] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 7 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a cloud service simulation method. Those skilled in the art can understand that Figure 7 the structure shown in

[0137] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout.

[0138] In an embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, it implements the steps in each of the above method embodiments.

[0139] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, it implements the steps in each of the above method embodiments.

[0140] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0141] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0142] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A cloud service simulation method, characterized in that: The method comprises: Generate a cloud service to be simulated according to the request type information carried by the cloud service simulation request; Outputting the target overall simulation data of the cloud service to be simulated through a preset data evaluation model, wherein the preset data evaluation model is iteratively trained based on the overall simulation data of all cloud services; The cloud service to be simulated is simulated according to the target overall simulation data.

2. The method according to claim 1, characterized in that The cloud service to be simulated includes an interactive cloud service to be simulated; and simulating the cloud service to be simulated according to the target overall simulation data includes: Obtaining target performance evaluation data of the interactive cloud service to be simulated; When the performance evaluation data meets the preset evaluation conditions, the interactive cloud service to be simulated is simulated according to the target overall simulation data and the target performance evaluation data.

3. The method according to claim 1, characterized in that After simulating the cloud service to be simulated according to the target overall simulation data, the method further includes: Splitting the target overall simulation data to obtain local simulation data corresponding to each of the multiple cloud service links of the cloud service to be simulated; Generating a plurality of simulation performance data of the cloud service to be simulated according to all the local simulation data; The plurality of simulated performance data are stored in a preset storage space.

4. The method according to claim 3, characterized in that The storing the simulated performance data into a preset storage space includes: Determining a performance evaluation index for each of the simulated performance data; By integrating multiple performance evaluation indicators, performance evaluation data of the cloud service to be simulated is obtained; The plurality of simulation performance data and the performance evaluation data are stored together in a preset storage space.

5. The method according to claim 1, characterized in that Before outputting the target overall simulation data of the to-be-simulated cloud service through the preset data evaluation model, the method further includes: Collect initial cloud service data in the preset data collection mode; Processing the cloud service initial data to obtain cloud service basic data; According to the cloud service basic data, overall simulation data of each cloud service is generated.

6. The method according to claim 5, characterized in that The processing of the cloud service initial data to obtain cloud service basic data includes: Desensitizing the cloud service initial data to obtain cloud service desensitized data; Deduplication processing is performed on the cloud service desensitized data to obtain cloud service deduplication data; The deduplicated cloud service data is subjected to noise reduction processing to obtain the cloud service basic data.

7. A cloud service simulation device, characterized in that: The device comprises: A generation module, used to generate a cloud service to be simulated according to the request type information carried by the cloud service simulation request; An output module, used to output the target overall simulation data of the cloud service to be simulated through a preset data evaluation model, wherein the preset data evaluation model is obtained by iterative training based on the overall simulation data of all cloud services; The simulation module is used to simulate the cloud service to be simulated according to the target overall simulation data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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