Cloud micro-service cluster management and control method, cloud server, storage medium and computer program product

Abstract

The invention relates to a management and control method of a cloud micro-service cluster, a cloud server, a storage medium and a computer program product. The method is applied to a cloud server, and comprises the following steps: determining difference resources of each cloud micro-service cluster of a power grid in resource characteristics, the resource characteristics comprising one or more of a computing resource, a storage resource, a network resource, an interface specification and a management mode; based on the difference resources of the cloud micro-service clusters, carrying out integration processing on the resources of the cloud micro-service clusters to obtain a resource set with a unified format and protocol; coordinating and uniformly processing the technical specifications of the existing micro-service framework of each cloud micro-service cluster to obtain an optimized service framework corresponding to each cloud micro-service cluster; and based on the optimized service framework, carrying out resource allocation on the resources in the resource set in each cloud micro-service cluster according to the overall business requirements and the difference resources of each cloud micro-service cluster.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a cloud microservice cluster management and control method, a cloud server, a storage medium, and a computer program product. Background Art

[0002] In the current field of heterogeneous cluster management, the control of cloud microservice clusters mainly relies on the traditional decentralized management model. Clusters built with different architectures and technologies operate independently according to their own resource management strategies. Computing resources are allocated based on local load conditions, storage resources are allocated according to pre-planned storage quotas, and network resources are dynamically adjusted based on local network traffic.

[0003] In terms of microservice framework, each cluster selects different frameworks according to its own business characteristics. For example, some use Spring Cloud-based frameworks, focusing on rapid development and convenient deployment of services; others use Kubernetes-related microservice architectures, focusing on containerized management and efficient resource utilization. In terms of service discovery and registration mechanisms, different frameworks have different implementation methods. Some use DNS resolution to achieve service positioning, and some register service information through a dedicated registration center. The communication protocols are also diverse. There are both HTTP-based RESTful protocols for regular data interaction and gRPC protocols to meet high-performance, low-latency communication requirements. The security framework is also built according to their respective security needs, covering a variety of authentication methods, such as password verification, token verification, etc. The authorization mechanism is customized according to business roles and permission levels, and the data encryption method uses different algorithms according to the sensitivity.

[0004] However, the existing cloud microservice cluster management technology in the field of heterogeneous cluster management has problems such as unbalanced resource allocation, low scheduling efficiency, difficulty in unifying the microservice framework, non-standard communication protocols, and great security risks, which restrict the application and development of cloud microservices. Summary of the invention

[0005] Based on this, it is necessary to provide a cloud microservice cluster management and control method, cloud server, storage medium and computer program product to address the above technical issues.

[0006] In a first aspect, the present application provides a method for managing and controlling a cloud microservice cluster, which is applied to a cloud server, including:

[0007] Determine the resource characteristics of each cloud microservice cluster of the power grid, wherein the resource characteristics include: one or more of computing resources, storage resources, network resources, interface specifications, and management modes;

[0008] Based on the different resources of each cloud microservice cluster, the resources of each cloud microservice cluster are integrated to obtain a resource set with a unified format and protocol;

[0009] Coordinate and unify the technical specifications of the existing microservice frameworks of the cloud microservice clusters to obtain optimized service frameworks corresponding to the cloud microservice clusters;

[0010] Based on the optimized service framework, resources in the resource set are allocated among the cloud microservice clusters according to the overall business requirements of the cloud microservice clusters and the differentiated resources.

[0011] In one embodiment, determining the difference in resource characteristics of each cloud microservice cluster of the power grid includes:

[0012] Extract data on the resource characteristics of each cloud microservice cluster of the power grid to obtain resource characteristic information of each cloud microservice cluster;

[0013] The resource characteristic information of each cloud microservice cluster is compared and analyzed to determine the difference in resource characteristics of each cloud microservice cluster.

[0014] In one embodiment, the integrating processing of the resources of each cloud microservice cluster based on the different resources of each cloud microservice cluster to obtain a resource set with a unified format and protocol includes:

[0015] Analyze the different resources of each cloud microservice cluster to determine the resources with inconsistent formats in each cloud microservice cluster;

[0016] According to the preset template, the resources with inconsistent formats in each cloud microservice cluster are formatted and regularized to obtain a preliminary resource set with a unified format;

[0017] Based on the interactive protocol specification, the preliminary resource set with unified format is subjected to protocol regularization processing to obtain a resource set with unified format and protocol.

[0018] In one embodiment, the technical specifications of the existing microservice frameworks of each cloud microservice cluster are coordinated and unified to obtain the optimized service frameworks corresponding to each cloud microservice cluster, including:

[0019] Collect technical specification information of existing microservice frameworks of each cloud microservice cluster, including historical connection data of service discovery and registration mechanisms, existing communication protocols, and existing security frameworks;

[0020] Determine the docking problem of the service discovery and registration mechanism according to the historical docking data; and update the process of the service discovery and registration mechanism based on the docking problem to obtain an optimized process;

[0021] Perform unified conversion and standardization on existing communication protocols to obtain optimized communication protocols;

[0022] The existing security framework includes: at least one of an identity authentication method, an authorization mechanism, and a data encryption method, and the existing security framework is integrated to obtain an optimized security framework;

[0023] By combining the optimization process, the optimized communication protocol and the optimized security framework, an optimized service framework corresponding to each cloud microservice cluster is obtained.

[0024] In one embodiment, based on the optimized service framework, resources in the resource set are allocated in each cloud microservice cluster according to the overall business requirements of each cloud microservice cluster and the differential resources, including:

[0025] Determine the demand trend information of the business demand for various resources according to the overall business demand of each cloud microservice cluster and the differentiated resources;

[0026] Obtaining actual usage and remaining availability of resources in the resource set;

[0027] Based on the optimized service framework, the resources in the resource set are allocated in each cloud microservice cluster according to the demand trend information and the actual usage and remaining availability of the resources in the resource set.

[0028] In one embodiment, after allocating resources in the resource set among the cloud microservice clusters based on the optimized service framework and the overall business requirements of the cloud microservice clusters and the differential resources, the method further includes:

[0029] Collecting operation status data of each cloud microservice cluster, wherein the operation status data includes: one or more of resource utilization, service response time, and operation stability;

[0030] When it is determined based on the operating status data of each cloud microservice cluster that there is a resource anomaly or service failure, a corresponding solution is called to perform repair processing.

[0031] In a second aspect, the present application also provides a cloud server, including:

[0032] A heterogeneous cluster management module is used to determine the different resources of each cloud microservice cluster of the power grid in terms of resource characteristics, wherein the resource characteristics include: one or more of computing resources, storage resources, network resources, interface specifications, and management modes; based on the different resources of each cloud microservice cluster, the resources of each cloud microservice cluster are integrated to obtain a resource set with a unified format and protocol;

[0033] A microservice framework consistency coordination module is used to coordinate and unify the technical specifications of the existing microservice frameworks of each cloud microservice cluster to obtain an optimized service framework corresponding to each cloud microservice cluster;

[0034] The resource allocation optimization module is used to allocate the resources in the resource set among the cloud microservice clusters based on the optimized service framework and according to the overall business requirements of the cloud microservice clusters and the differential resources.

[0035] In a third aspect, the present application further provides a cloud server, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0036] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect.

[0037] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0038] The above-mentioned cloud microservice cluster management and control method, applied to a cloud server, can determine the different resources of each cloud microservice cluster of the power grid in terms of resource characteristics, wherein the resource characteristics include: one or more of computing resources, storage resources, network resources, interface specifications, and management modes; based on the different resources of each cloud microservice cluster, the resources of each cloud microservice cluster are integrated to obtain a resource set with a unified format and protocol; the technical specifications of the existing microservice framework of each cloud microservice cluster are coordinated and unified to obtain an optimized service framework corresponding to each cloud microservice cluster; based on the optimized service framework, the resources in the resource set are allocated among the cloud microservice clusters according to the overall business needs of each cloud microservice cluster and the different resources. This solution solves the problem of uneven resource allocation by accurately determining the different resources of each cloud microservice cluster in the power grid in terms of multiple resource characteristics, and then integrating the resources to obtain a collection with unified format and protocol; it coordinates and unifies the technical specifications of the existing microservice framework to form an optimized service framework, overcomes the dilemma of difficult unification of the microservice framework, and at the same time improves the standardization of communication protocols and eliminates the obstacles of non-standard communication protocols; it allocates resources on demand based on the optimized framework, greatly improving scheduling efficiency, and by strengthening the security of the framework to avoid major security risks, it promotes the widespread, stable application and vigorous development of cloud microservices in various industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only 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.

[0040] Figure 1 A schematic diagram of a process of a cloud microservice cluster management and control method in one embodiment;

[0041] Figure 2 A flowchart of another method for controlling a cloud microservice cluster in one embodiment;

[0042] Figure 3 A flowchart of a method for controlling a cloud microservice cluster in an embodiment is shown;

[0043] Figure 4 It is a structural block diagram of a cloud server in one embodiment;

[0044] Figure 5 is a structural block diagram of a heterogeneous cluster management module 401 in one embodiment;

[0045] Figure 6It is a structural block diagram of a microservice framework consistency coordination module 402 in one embodiment;

[0046] Figure 7 is a structural block diagram of a resource allocation optimization module 403 in an embodiment;

[0047] Figure 8 is a structural block diagram of a scheduling coordination module 405 in one embodiment;

[0048] Fig. 9 is a structural block diagram of a unified supervision module 404 in one embodiment;

[0049] Fig.10 1 is a diagram of the internal structure of a cloud server in one embodiment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying 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.

[0051] With the vigorous development of cloud computing and microservices, many companies have devoted themselves to the application exploration of cloud microservices. However, in the key field of cloud microservice cluster integration control, the current technologies have exposed many problems that need to be overcome urgently.

[0052] First, let's focus on the management of heterogeneous clusters. The so-called "heterogeneous cluster" refers to a combination of clusters built with different architectures and various technologies. These heterogeneous clusters have significant differences in many key aspects: first, the resource characteristics are significantly different, covering computing resources (such as the server's CPU computing power, GPU parallel computing capabilities, and other capabilities for data processing), storage resources (such as hard disk storage capacity, storage read and write speed, and other resources for data storage), and network resources (such as network bandwidth, network latency, and other conditions that determine the speed and smoothness of data transmission); second, the interface specifications are different, which involves the inconsistent interface standards followed when different clusters connect and interact with each other; third, the management modes are different, and the resource allocation, task scheduling, and other management methods adopted by each cluster are very different.

[0053] This has led to a series of thorny problems. On the one hand, the imbalance of resource allocation has frequently occurred. Due to the lack of an optimized configuration strategy that can coordinate global business needs, some areas have been in urgent need of resources due to business concentration, while other areas have been left idle due to scarcity of business, resulting in serious waste. For example, when dealing with some critical business tasks that have extremely high demand for computing resources, due to the lack of planning for computing resource allocation from an overall perspective, the computing resources required for critical businesses are often not met, which in turn delays the business process and undermines the smoothness and timeliness of the overall business process. On the other hand, in the scheduling link, due to the lack of a unified coordination mechanism, it is difficult to respond quickly and efficiently to the dynamic changes in the business, and the scheduling efficiency is extremely low. Furthermore, at the regulatory level, given the huge differences between clusters, it is extremely difficult to implement an effective unified regulatory strategy, and many potential problems cannot be detected and properly resolved in a timely manner.

[0054] Secondly, we focus on the consistency of microservice frameworks. In heterogeneous environments, it is difficult for existing microservice frameworks to achieve unified technical specifications. Specifically, the service discovery and registration mechanisms cannot be seamlessly connected in different operating environments, just like people from different countries use different languages ​​to communicate, which hinders communication and hinders the dynamic deployment and expansion of microservices, making it impossible for new microservices to be flexibly and quickly integrated into the existing system; the communication protocol lacks a unified standard, just like trains with different railway gauges cannot run on the same track, the communication between services is inefficient and unstable, and the conversion between different protocols is even more difficult, which undoubtedly greatly increases the complexity of the system; the harm caused by inconsistent security frameworks is more serious, and the authentication methods (such as passwords, fingerprints, facial recognition and other means of confirming user identity), authorization mechanisms (rule settings that specify which users can access which resources) and data encryption methods (algorithms that convert data into ciphertext to protect data security) are different, which provides opportunities for malicious attackers, not only causing management costs to soar and management difficulties to increase, but also leading to a significant reduction in development efficiency, which seriously restricts the steady development of cloud microservices.

[0055] To sum up, in view of the many defects of existing technologies in the integrated management and control of cloud microservice clusters, there is an urgent need for a cloud microservice cluster management and control method that can achieve efficient, stable and reliable cluster management goals, so as to provide guarantee for the widespread application and healthy development of cloud microservices in all walks of life.

[0056] In order to solve the above problems, the embodiment of the present application provides a cloud microservice cluster management and control method, which is applied to cloud servers. Through this solution, the different resources of each cloud microservice cluster of the power grid in multiple resource characteristics are accurately determined, and then the resources are integrated to obtain a collection with unified format and protocol, thereby solving the problem of uneven resource allocation; the technical specifications of the existing microservice framework are coordinated and unified to form an optimized service framework, which overcomes the dilemma of difficult unification of the microservice framework, while improving the standardization of the communication protocol and eliminating the obstacle of non-standard communication protocols; resources are allocated on demand based on the optimized framework, which greatly improves the scheduling efficiency, and also avoids the risk of major security risks by strengthening the framework security, thereby promoting the widespread, stable application and vigorous development of cloud microservices in various industries in all aspects.

[0057] In an exemplary embodiment, Figure 1 As shown, a flow chart of a method for controlling a cloud microservice cluster is provided. The method can be executed by a cloud server. A cloud microservice cluster control platform can be configured in the cloud server. The steps of the method can be specifically executed by the cloud microservice cluster control platform. The method includes the following steps:

[0058] 101. Determine the different resource characteristics of each cloud microservice cluster of the power grid.

[0059] The resource characteristics include: one or more of computing resources, storage resources, network resources, interface specifications, and management modes.

[0060] In some embodiments, determining the difference in resource characteristics of each cloud microservice cluster of the power grid includes: extracting data on the resource characteristics of each cloud microservice cluster of the power grid to obtain resource characteristic information of each cloud microservice cluster; and performing comparative analysis on the resource characteristic information of each cloud microservice cluster to determine the difference in resource characteristics of each cloud microservice cluster.

[0061] In one possible implementation method: first, it is necessary to use a special data collection tool to extract data on the resource characteristics of each cloud microservice cluster of the power grid; then, use a data analysis algorithm to compare and analyze the collected resource characteristic information of each cloud microservice cluster.

[0062] The above-mentioned data collection tool can be a program developed based on software agent technology. It is deployed on each cluster node and can collect real-time information such as the server's CPU usage, memory usage (belonging to the category of computing resources), hard disk used space, read and write rates (reflecting storage resource conditions), network interface real-time bandwidth, data packet transmission delay (reflecting network resource status), and each cluster's external interaction interface specification description documents, internal resource management model documents, and other information, so as to obtain resource characteristic information of each cloud microservice cluster.

[0063] Among them, data analysis algorithms cover a variety of methods for processing, parsing, and extracting valuable information from data. In the cloud microservice cluster management scenario, data analysis algorithms undertake key tasks. The massive resource characteristic information collected from the data collection tool, such as the CPU usage, memory usage, storage read and write rate, network bandwidth data of each cluster node, as well as text information such as interface specifications and management mode descriptions, all need to be sorted out by data analysis algorithms. It can clean these heterogeneous data and remove invalid, erroneous or duplicate data records, such as removing obviously abnormal CPU usage data points caused by sensor failure. It can also convert data to make data of different formats and dimensions comparable, such as converting storage data in bytes to megabytes. Through these preprocessing operations, the foundation is laid for subsequent precise analysis to fully understand the operating status and resource distribution of each cloud microservice cluster.

[0064] For example, cluster analysis algorithms based on data mining are used to group clusters with similar resource characteristics into one category, and by comparing key indicators between different categories, such as the mean difference in the number of CPU cores between different clusters, the standard deviation of storage capacity, etc., the differences in resource characteristics of each cloud microservice cluster can be accurately determined. This analysis is helpful for subsequent targeted resource allocation and management.

[0065] The core principle of the above-mentioned clustering analysis algorithm based on data mining is to divide the data set into different clusters or classes based on the similarity between data objects. When determining the differences in resource characteristics of the power grid cloud microservice cluster, some key features are first selected as the basis for clustering, such as the number of CPU cores and main frequency for computing resources, storage capacity and read and write speed for storage resources, and bandwidth and latency for network resources. The algorithm calculates the distance measurement between these features, such as the Euclidean distance and Manhattan distance. Taking the Euclidean distance as an example, if the numerical differences between two clusters in multiple dimensions such as the number of CPU cores and storage capacity are small, the Euclidean distance between them is short, which means that the similarity is high and they will be classified into the same cluster. By continuously iterating this process, clusters with similar resource characteristics are eventually clustered together to form different categories. Once the clustering is completed, by comparing the statistics such as the mean and standard deviation of key indicators between different clusters, the differences in resource characteristics of each cloud microservice cluster can be clearly found. For example, one cluster may have fewer CPU cores on average but larger storage capacity, while another cluster may have the opposite. This provides precise direction for subsequent targeted resource allocation and cluster configuration optimization, avoids unreasonable allocation and waste of resources, and improves the operating efficiency of the entire cloud microservice cluster system.

[0066] In another possible implementation, a centralized control platform (such as the control platform of the cloud microservice cluster mentioned above) can be built, which establishes a secure and reliable communication connection with each cloud microservice cluster, for example, through a network channel based on the Secure Sockets Layer (SSL) / Transport Layer Security (TLS) encryption protocol to ensure the security of data transmission. The platform sends resource feature query instructions to each cluster. After receiving the instructions, each cluster feeds back its own resource feature data in a preset data format, which covers the various types of resource information mentioned above. After receiving the data of all clusters, the control platform uses the built-in comparison module to intuitively present the numerical comparison of each cluster in each dimension of resource features in a table form, and then determine the differential resources. The "comparison module" here can be a program developed based on a data analysis library (such as Pandas) based on Python (a widely used high-level programming language), which can efficiently handle large-scale data comparison tasks.

[0067] 102. Based on the different resources of each cloud microservice cluster, the resources of each cloud microservice cluster are integrated to obtain a resource set with a unified format and protocol.

[0068] In some embodiments, based on the differential resources of each cloud microservice cluster, the resources of each cloud microservice cluster are integrated to obtain a resource set with unified format and protocol, including: analyzing the differential resources of each cloud microservice cluster to determine the resources with inconsistent formats in each cloud microservice cluster; according to a preset template, formatting the resources with inconsistent formats in each cloud microservice cluster to obtain a preliminary resource set with unified format; based on the interaction protocol specification, protocol-regularizing the preliminary resource set with unified format to obtain a resource set with unified format and protocol.

[0069] In one possible implementation, we deeply analyze the different resources of each cloud microservice cluster and identify the resources with inconsistent formats in each cloud microservice cluster by identifying the characteristics of the resource format. For example, for data files in storage resources, some clusters use comma-separated values ​​(Comma-Separated Values,

[0070] CSV) format to store data records, while others use object notation (JavaScript ObjectNotation,

[0071] JSON) format, and identify these format differences through file header identifiers, data structure characteristics, etc. For the identified resources with inconsistent formats, the formats are regularized according to the preset template. The "preset template" here can be a pre-defined general data format standard, for example, it stipulates that the eXtensible Markup Language (XML) format is uniformly used to store structured data, and a special format conversion tool is developed to convert data in CSV or JSON format according to the grammatical rules of XML, so as to obtain a preliminary resource set with a unified format.

[0072] In another possible implementation, an automated resource integration software platform is used, which integrates a format recognition engine, a template matching module, and a protocol adapter component. After inputting the different resources of each cloud microservice cluster, the format recognition engine uses the pattern recognition algorithm in machine learning to quickly identify the format types of different resources, such as the format of text resources and the format of binary resources. The template matching module selects the most suitable template to convert the resources based on the built-in multiple standard format templates to obtain a resource set with a unified intermediate format. Finally, the protocol adapter component adapts the resources in the intermediate resource set according to the selected interactive protocol based on the pre-configured interactive protocol library, such as the library containing common protocols such as Hypertext Transfer Protocol Version 2 (HTTP / 2) and WebSocket (a network protocol for full-duplex communication on a single Transmission Control Protocol (TCP) connection), and generates a resource set with a unified format and protocol, so as to facilitate subsequent unified management and deployment.

[0073] 103. Coordinate and unify the technical specifications of the existing microservice frameworks of each cloud microservice cluster to obtain the optimized service framework corresponding to each cloud microservice cluster.

[0074] In some embodiments, Figure 2 As shown, a flow chart of another method for controlling a cloud microservice cluster is provided, and the technical specifications of the existing microservice frameworks of each cloud microservice cluster are coordinated and processed in a unified manner to obtain an optimized service framework corresponding to each cloud microservice cluster, including the following steps:

[0075] 103a. Collect technical specification information of the existing microservice framework of each cloud microservice cluster, including historical connection data of service discovery and registration mechanism, existing communication protocols and existing security framework.

[0076] In one possible implementation, special information collection tools, such as web crawler technology combined with API calls, are used to collect technical specification information of existing microservice frameworks in each cloud microservice cluster. For historical docking data of service discovery and registration mechanisms, the timestamps, identification of successful docking, error codes, and other information of previous service docking processes are extracted by accessing the operation and maintenance log database of each cluster; for existing communication protocols, parameters such as protocol type, port number, and data transmission format are read from the configuration files of each cluster; for existing security frameworks, security configuration files are parsed to obtain information such as authentication methods (such as common username / password authentication, authentication based on digital certificates), authorization mechanisms (role-based access control or attribute-based access control), and data encryption methods.

[0077] 103b. Determine the docking problems of the service discovery and registration mechanism based on the historical docking data, and update the process of the service discovery and registration mechanism based on the docking problems to obtain an optimized process.

[0078] In one possible implementation, based on the collected historical docking data, manually analyze or use a fault diagnosis algorithm to determine the docking problem of the service discovery and registration mechanism. For example, if a large number of docking failure records are found to be concentrated in a certain time period, and the error code points to a network timeout problem, it may mean that there is a defect in the network configuration. Based on the docking problem, the process of the service discovery and registration mechanism is updated, such as optimizing network configuration parameters, adjusting the service heartbeat detection interval, etc., to obtain an optimized process.

[0079] 103c. Perform unified conversion and standardization on existing communication protocols to obtain optimized communication protocols.

[0080] In one possible implementation, the protocol conversion middleware is used to uniformly convert and standardize the existing communication protocols. The middleware has built-in conversion rules for multiple common communication protocols. For example, when converting a simple communication protocol based on UDP to a reliable communication protocol based on TCP, the data packet is repackaged and the transmission is controlled according to the three-way handshake and data confirmation mechanisms of the TCP protocol to ensure the reliability of data transmission and obtain an optimized communication protocol.

[0081] 103d. Unify and integrate the existing security framework to obtain an optimized security framework.

[0082] The existing security framework includes at least one of an identity authentication method, an authorization mechanism and a data encryption method.

[0083] In one possible implementation method: for the unified integration of existing security frameworks, a security framework fusion tool is developed. If one cluster uses username / password authentication and the other cluster uses digital certificate authentication, the fusion tool can uniformly access the username / password authentication entry at the front end, and associate and map the user information that has passed the username / password authentication with the digital certificate at the back end to achieve a unified identity authentication method; for the authorization mechanism, different rules are sorted out, common rules are extracted, and a unified authorization decision engine is built; for the data encryption method, a high-intensity and compatible encryption algorithm is selected as the standard, and the data that needs to be encrypted is uniformly processed using this algorithm to obtain an optimized security framework.

[0084] 103e. By combining the optimized process, the optimized communication protocol and the optimized security framework, an optimized service framework corresponding to each cloud microservice cluster is obtained.

[0085] In one possible implementation method: the optimized process, optimized communication protocol and optimized security framework are organically combined, and embedded into the microservice framework corresponding to each cloud microservice cluster through configuration file updates, code module replacement, etc., to obtain the optimized service framework corresponding to each cloud microservice cluster.

[0086] In one possible implementation, a cloud microservice framework control center is deployed, which establishes a two-way communication link with each cloud microservice cluster and collects the microservice framework technical specification information of each cluster by sending standardized information collection instructions. The control center has a built-in intelligent analysis engine, which uses big data analysis technology to deeply mine historical docking data and automatically discover potential docking problems of service discovery and registration mechanisms. For example, the frequent occurrence of repeated registration may be due to the imperfect deduplication mechanism of the registration center. Then, targeted optimization process suggestions are generated, and the operation and maintenance personnel make manual adjustments or perform adjustments through automated scripts based on the suggestions. The elastic computing resources provided by the cloud computing platform are used to run the unified protocol conversion service. The service is deployed based on containerization technology. Each container is responsible for the conversion task of a communication protocol. When it is detected that the communication protocol of a cluster needs to be converted, the communication traffic of the cluster is dynamically routed to the corresponding container for protocol conversion, and the optimized communication protocol is obtained efficiently. For the unified integration of the security framework, a distributed security trust system is built using blockchain technology. The identity authentication, authorization mechanism and data encryption information of each cluster are uploaded to the chain, and the consensus mechanism of the blockchain is used to ensure the consistency and non-tamperability of the information. Smart contracts are developed on the chain to achieve mutual recognition of identity authentication, unified coordination of authorization mechanisms, and standardization of data encryption to obtain an optimized security framework. Finally, the control center will send the generated optimized service framework to each cloud microservice cluster in the form of a software package. Each cluster will use automated deployment tools to quickly update and replace the framework to obtain the corresponding optimized service framework.

[0087] 104. Based on the optimized service framework, resources in the resource set are allocated among the cloud microservice clusters according to the overall business requirements and differentiated resources of each cloud microservice cluster.

[0088] In some embodiments, in some embodiments, as Figure 3 As shown, a flow chart of a method for controlling a cloud microservice cluster is provided. Based on the above optimized service framework, according to the overall business requirements and differentiated resources of each cloud microservice cluster, resources in the resource set are allocated in each cloud microservice cluster, including the following steps:

[0089] 104a. According to the overall business requirements and differentiated resources of each cloud microservice cluster, determine the demand tendency information of business requirements for various resources.

[0090] In one possible implementation method: Based on the overall business needs and differentiated resources of each cloud microservice cluster, a business demand modeling tool is used for modeling and analysis. For example, for a power grid cloud microservice cluster that includes power load forecasting, power equipment monitoring and other businesses, the demand trend information for various resources is determined by analyzing factors such as business processes, data flows, and task priorities. For example, the power load forecasting business has a high demand for GPU computing power in computing resources because it involves parallel computing of a large amount of data; the power equipment monitoring business has a high real-time requirement for network resources, and requires low-latency network transmission to ensure timely data feedback.

[0091] In one possible implementation, an intelligent resource allocation platform is built, which is deeply integrated with the optimized service framework and receives the business demand change information of each cloud microservice cluster in real time. The deep learning algorithm in artificial intelligence is used to analyze the correlation between business demand and differentiated resources, and automatically learn the demand pattern for various resources in different business scenarios, so as to determine the demand tendency information of business demand for various resources.

[0092] 104b. Obtain the actual usage and remaining availability of resources in the resource collection.

[0093] In a possible implementation, the actual usage and remaining availability of resources in the resource set are obtained through a resource monitoring system. The monitoring system can be built based on open source monitoring software, and collects various resource indicators in real time, such as CPU usage, memory idleness, network bandwidth utilization, etc., and stores these data in a database for query and call at any time.

[0094] In one possible implementation, the actual usage and remaining availability of resources in the resource collection can be directly obtained by using the resource management API of the cloud computing platform, without the need to build an additional monitoring system. These APIs can provide high-precision resource data and have the characteristics of strong real-time performance and easy call.

[0095] 104c. Based on the above optimized service framework, the resources in the resource set are allocated in each cloud microservice cluster according to the demand tendency information and the actual usage and remaining availability of the resources in the resource set.

[0096] In a possible implementation: Based on the above optimized service framework, using the resource allocation algorithm, according to the demand tendency information and the actual usage and remaining availability of the resources in the resource set, the resources in the resource set are allocated in each cloud microservice cluster. For example, a resource allocation strategy based on a greedy algorithm is adopted to give priority to clusters with high business demand tendency and sufficient remaining available resources, and the resource allocation plan is gradually optimized to ensure the efficient operation of the overall business.

[0097] In one possible implementation, a reinforcement learning-based resource allocation model is run on the intelligent resource allocation platform constructed above. The model regards the resource allocation process as a Markov decision process. Through continuous trial and error and learning, the resource allocation strategy is dynamically adjusted according to demand tendency information and the actual usage and remaining availability of resources in the resource collection. The resources in the resource collection are optimally allocated in each cloud microservice cluster to adapt to the ever-changing business needs.

[0098] The above-mentioned cloud microservice cluster management and control method is applied to cloud servers. By accurately determining the different resources of various cloud microservice clusters in the power grid in terms of multiple resource characteristics, and then integrating the resources to obtain a collection with unified format and protocol, the problem of unbalanced resource allocation is solved; the technical specifications of the existing microservice framework are coordinated and unified to form an optimized service framework, which overcomes the dilemma of difficult unification of the microservice framework, while improving the standardization of communication protocols and eliminating the obstacles of non-standard communication protocols; resources are allocated on demand based on the optimized framework, which greatly improves the scheduling efficiency, and by strengthening the security of the framework, the risk of major security risks is avoided, thereby promoting the widespread, stable application and vigorous development of cloud microservices in various industries in all aspects.

[0099] In some embodiments, based on the optimized service framework, after allocating resources in the resource set among each cloud microservice cluster according to the overall business needs and differentiated resources of each cloud microservice cluster, the cloud microservice cluster management and control method provided in the embodiment of the present application also includes: collecting operating status data of each cloud microservice cluster, the operating status data including: one or more of resource utilization, service response time, and operating stability; when it is determined that there is a resource anomaly or service failure based on the operating status data of each cloud microservice cluster, calling corresponding solutions for repair processing.

[0100] The above-mentioned resource anomalies may include but are not limited to:

[0101] CPU resource anomaly: When the CPU usage rate remains high for a long time, such as continuously above 90%, and there is no sudden large-scale parallel task demand in the system, it is an anomaly. This indicates that the CPU resource allocation is unreasonable or there are over-occupied programs, which makes the CPU continuously run at high load and has no time to process new tasks, causing tasks to queue and system response delays.

[0102] Memory resource anomaly: If the memory usage rate continues to rise, frequently hitting the system's preset memory warning threshold, or even causing the system to start using virtual memory for data exchange, this is a typical memory resource anomaly. It is usually caused by a memory leak, that is, the program does not release the memory correctly after applying for it, or some services occupy too much memory, causing the available memory to approach zero, which may cause the program to crash at any time.

[0103] Disk input and output (I / O) resource anomalies: If the read / write rate is too low for a long time, such as being continuously lower than 50% of the normal read / write speed, it is likely that the disk has hardware problems such as bad sectors and head failures, which make it difficult to read data. If the read / write rate is too high, such as continuously exceeding the normal peak by more than 2 times and is not caused by reasonable data batch processing needs, it is likely that there are unreasonable frequent reads and writes, such as the program falling into an infinite loop for invalid reads and writes, which occupies a large amount of system resources and slows down the performance of the entire cluster.

[0104] The above-mentioned service failure situations may include but are not limited to:

[0105] Response time failure: Under established business rules, services have clear response time requirements, such as the order service on an e-commerce platform must respond within 1 second. When the timing code segment in the key link repeatedly records a response time far exceeding this threshold, such as multiple delays of 3 seconds or more, it means that the service has failed. The reasons are mostly slow responses from backend-related services, such as a sudden drop in database query efficiency due to index failure, table locks, etc., or high latency in network transmission due to insufficient bandwidth, routing failures, etc. It may also be that there are defects in the service's own business logic, and the algorithm complexity is too high, resulting in slow execution.

[0106] Operational stability failure: If cluster node processes are frequently monitored to terminate and restart unexpectedly, such as if a service process restarts more than 5 times within 10 minutes, it can be basically determined that the service has an operational stability failure. This is usually caused by internal program vulnerabilities, such as null pointer exceptions, array out-of-bounds errors, or the process cannot run stably due to memory overflow and resource competition; in terms of network connection, if the number of interruptions per minute exceeds 10 times, far exceeding the range allowed by normal network jitter, it will cause frequent interruptions in communication between services, data transmission will be blocked, and services will be unable to work together, which also falls into the category of service failure.

[0107] In the above process of collecting the running status data of each cloud microservice cluster, lightweight agent probes can be deployed on the nodes of each cloud microservice cluster. These agent probes are similar to the data collection tools mentioned above, but they focus more on capturing the real-time running status. For example, collect detailed usage of resources such as CPU, memory, and disk I / O to accurately calculate resource utilization. For the collection of service response time, a special timing code segment can be embedded in the key link of the service call. When a service request is initiated and responded to, this code automatically records the request initiation time and the response return time. The difference between the two is the service response time. The collection of running stability data can be comprehensively judged by monitoring indicators such as the process status of cluster nodes and the number of network connection interruptions.

[0108] Among them, the agent probe is a small program deployed on the target system (such as a cloud microservice cluster node) to collect specific system information and feed it back to the control center. It has the characteristics of strong real-time and high targeting, and can deeply obtain the detailed usage of the underlying resources.

[0109] In some embodiments, a centralized data collection platform can also be used, which establishes an efficient network connection with each cluster, such as HTTP / 2, and regularly sends data collection instructions to the cluster. The cluster feeds back relevant data such as resource utilization, service response time, and operation stability in a predetermined data format. This platform usually has certain data caching and preprocessing capabilities, and can perform preliminary cleaning on the collected data to remove obvious outliers, such as resource utilization data points with extremely high errors due to short-term network fluctuations. The collected data can use encryption protocols to ensure the confidentiality and integrity of the data during transmission to prevent the data from being stolen or tampered with. For the collected data, a distributed database can be used for data storage. It has high scalability and good read and write performance, and is suitable for storing large-scale cluster operation status data. The data is stored in time series to facilitate subsequent query and analysis.

[0110] In the cloud microservice cluster control method provided in the above embodiment of the present application, based on the collected data, it is possible to not only accurately determine whether the resources are abnormal and whether the service has any faults, but also to call corresponding solutions as soon as the problem occurs, thus laying a solid foundation for the stable and efficient operation of the cloud microservice cluster.

[0111] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0112] Based on the same inventive concept, the embodiment of the present application also provides a cloud server for implementing the above-mentioned cloud microservice cluster control method. The implementation solution provided by the cloud server to solve the problem is similar to the implementation solution recorded in the above-mentioned method, so the specific limitations in one or more cloud server embodiments provided below can refer to the limitations of the cloud microservice cluster control method above, and will not be repeated here.

[0113] In an exemplary embodiment, Figure 4 As shown, a structural block diagram of a cloud server is provided, including:

[0114] The heterogeneous cluster management module 401 is used to determine the different resources of each cloud microservice cluster of the power grid in terms of resource characteristics, wherein the resource characteristics include: one or more of computing resources, storage resources, network resources, interface specifications, and management modes; based on the different resources of each cloud microservice cluster, the resources of each cloud microservice cluster are integrated to obtain a resource set with a unified format and protocol;

[0115] The microservice framework consistency coordination module 402 is used to coordinate and unify the technical specifications of the existing microservice frameworks of the cloud microservice clusters to obtain the optimized service frameworks corresponding to the cloud microservice clusters;

[0116] The resource allocation optimization module 403 is used to allocate the resources in the resource set among the cloud microservice clusters based on the optimized service framework and according to the overall business requirements of the cloud microservice clusters and the differential resources.

[0117] In some embodiments, the heterogeneous cluster management module 401 is specifically used to:

[0118] Extract data on the resource characteristics of each cloud microservice cluster of the power grid to obtain resource characteristic information of each cloud microservice cluster;

[0119] The resource characteristic information of each cloud microservice cluster is compared and analyzed to determine the difference in resource characteristics of each cloud microservice cluster.

[0120] In some embodiments, the heterogeneous cluster management module 401 is specifically used to:

[0121] Analyze the different resources of each cloud microservice cluster to determine the resources with inconsistent formats in each cloud microservice cluster;

[0122] According to the preset template, the resources with inconsistent formats in each cloud microservice cluster are formatted and regularized to obtain a preliminary resource set with a unified format;

[0123] Based on the interactive protocol specification, the preliminary resource set with unified format is subjected to protocol regularization processing to obtain a resource set with unified format and protocol.

[0124] In some embodiments, the microservice framework consistency coordination module 402 is specifically used to:

[0125] Collect technical specification information of existing microservice frameworks of each cloud microservice cluster, including historical connection data of service discovery and registration mechanisms, existing communication protocols, and existing security frameworks;

[0126] Determine the docking problem of the service discovery and registration mechanism according to the historical docking data; and update the process of the service discovery and registration mechanism based on the docking problem to obtain an optimized process;

[0127] Perform unified conversion and standardization on existing communication protocols to obtain optimized communication protocols;

[0128] The existing security framework includes: at least one of an identity authentication method, an authorization mechanism, and a data encryption method, and the existing security framework is integrated to obtain an optimized security framework;

[0129] By combining the optimization process, the optimized communication protocol and the optimized security framework, an optimized service framework corresponding to each cloud microservice cluster is obtained.

[0130] In some embodiments, the resource allocation optimization module 403 is specifically configured to:

[0131] Determine the demand trend information of the business demand for various resources according to the overall business demand of each cloud microservice cluster and the differentiated resources;

[0132] Obtaining actual usage and remaining availability of resources in the resource set;

[0133] Based on the optimized service framework, the resources in the resource set are allocated in each cloud microservice cluster according to the demand trend information and the actual usage and remaining availability of the resources in the resource set.

[0134] In some embodiments, the cloud server further includes: a unified monitoring module 404, which is used to collect the operation status data of each cloud microservice cluster, and the operation status data includes: one or more of resource utilization, service response time, and operation stability;

[0135] When it is determined based on the operating status data of each cloud microservice cluster that there is a resource anomaly or service failure, a corresponding solution is called to perform repair processing.

[0136] The unified supervision module 404 can formulate and implement effective unified supervision strategies based on the commonalities and differences among the cloud microservice clusters, timely discover and resolve potential problems, and ensure the stable operation of the cloud microservice clusters.

[0137] In some embodiments, the cloud server may further include: a scheduling and coordination module 405, which is used to establish a unified coordination mechanism, improve scheduling efficiency, enable it to quickly respond to business changes, and ensure the timeliness and effectiveness of scheduling between cloud microservice clusters.

[0138] As shown in FIG5 , it is a structural block diagram of a heterogeneous cluster management module 401. Figure 5 The heterogeneous cluster management module 401 includes:

[0139] Difference analysis submodule 4011, resource integration submodule 4012 and interface adaptation submodule 4013, wherein:

[0140] The difference analysis submodule 4011 performs a detailed analysis of resource characteristics, interface specifications, and management modes of different cloud microservice clusters, sorts out various differences, and transmits the analysis results to other related submodules;

[0141] The resource integration submodule 4012 integrates the computing resources, storage resources, network resources, etc. of each cloud microservice cluster according to the difference analysis results, in preparation for subsequent unified deployment;

[0142] The interface adapter submodule 4013 constructs an adaptation mechanism based on the differences in interface specifications of different cloud microservice clusters to ensure data interaction and collaborative management between the cloud microservice clusters.

[0143] As shown in FIG6 , it is a structural block diagram of a microservice framework consistency coordination module 402. Figure 6 The microservice framework consistency coordination module 402 includes: a service docking submodule 4021, a protocol specification submodule 4022 and a security unification submodule 4023, wherein:

[0144] The service docking submodule 4021 is responsible for handling the docking work of service discovery and registration mechanisms in different operating environments, ensuring that microservices can be dynamically deployed and expanded smoothly;

[0145] The protocol specification submodule 4022 is used to formulate a unified communication protocol, improve the efficiency and stability of communication between services, solve the problem of difficulty in converting different protocols, and reduce system complexity;

[0146] The security unification submodule 4023 is used to unify identity authentication, authorization mechanism and data encryption method, eliminate the potential security risks caused by inconsistent security frameworks, and improve the security of the system.

[0147] For example, Figure 7 As shown, it is a structural block diagram of a resource allocation optimization module 403. Figure 7 The resource allocation optimization module 403 includes: a demand analysis submodule 4031, a resource evaluation submodule 4032 and an allocation strategy generation submodule 4033, wherein:

[0148] The demand analysis submodule 4031 is used to collect and analyze overall business needs and determine the needs of various businesses for computing resources, storage resources, network resources, etc.;

[0149] The resource evaluation submodule 4032 is used to evaluate the existing resources of each cluster to understand the actual usage and remaining availability of resources;

[0150] The allocation strategy generation submodule 4033 is used to generate a reasonable resource allocation strategy based on the results of demand analysis and resource evaluation to achieve balanced allocation of resources among clusters.

[0151] For example, as shown in FIG8 , it is a structural block diagram of a scheduling coordination module 405. Figure 8 The scheduling and coordination module 405 includes: a service perception submodule 4051, a coordination mechanism submodule 4052 and a response execution submodule 4053, wherein:

[0152] The business perception submodule 4051 monitors business changes in real time, obtains business demand information for resource scheduling in a timely manner, and transmits it to the coordination mechanism submodule 4052;

[0153] The coordination mechanism submodule 4052 starts a unified coordination mechanism based on the business demand information and plans a reasonable scheduling plan;

[0154] The response execution submodule 4053 quickly executes the scheduling operation according to the scheduling plan formulated by the coordination mechanism submodule 4052 to ensure the smooth development of the business.

[0155] As shown in FIG9 , a structural block diagram of a unified supervision module 404 is shown. Fig. 9 The unified supervision module 404 includes:

[0156] Strategy formulation submodule 4041, problem monitoring submodule 4042 and processing execution submodule 4043, wherein:

[0157] The strategy formulation submodule 4041 comprehensively considers the characteristics and commonalities of each cloud microservice cluster, formulates a comprehensive and effective unified supervision strategy, and clarifies the focus and rules of supervision;

[0158] The problem monitoring submodule 4042 monitors the operation status of each cloud microservice cluster in real time according to the formulated supervision strategy, promptly discovers potential problems, such as resource anomalies, service failures, etc., and transmits the problem information to the processing execution submodule 4043;

[0159] The processing execution submodule 4043 takes corresponding solutions in a timely manner for the problems found by the problem monitoring submodule 4042 to ensure the stable operation of each cloud microservice cluster.

[0160] Each module in the above cloud server can be implemented in whole or in part by software, hardware and their combination. Each module can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each module above.

[0161] In an exemplary embodiment, a cloud server is provided, and the internal structure diagram of the cloud server can be as follows: Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for managing and controlling a cloud microservice cluster is implemented.

[0162] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present 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 arrangement of components.

[0163] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the cloud microservice cluster management and control method provided in the above method embodiment is implemented.

[0164] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the cloud microservice cluster management and control method provided in the above method embodiment is implemented.

[0165] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the cloud microservice cluster management and control method provided in the above method embodiment.

[0166] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0167] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0168] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for controlling a cloud microservice cluster, characterized in that: Applied to cloud servers, including: Determine the resource characteristics of each cloud microservice cluster of the power grid, wherein the resource characteristics include: one or more of computing resources, storage resources, network resources, interface specifications, and management modes; Based on the different resources of each cloud microservice cluster, the resources of each cloud microservice cluster are integrated to obtain a resource set with a unified format and protocol; Coordinate and unify the technical specifications of the existing microservice frameworks of the cloud microservice clusters to obtain optimized service frameworks corresponding to the cloud microservice clusters; Based on the optimized service framework, resources in the resource set are allocated among the cloud microservice clusters according to the overall business requirements of the cloud microservice clusters and the differentiated resources.

2. The method according to claim 1, characterized in that: The determining of the difference in resource characteristics of each cloud microservice cluster of the power grid includes: Extract data on the resource characteristics of each cloud microservice cluster of the power grid to obtain resource characteristic information of each cloud microservice cluster; The resource characteristic information of each cloud microservice cluster is compared and analyzed to determine the difference in resource characteristics of each cloud microservice cluster.

3. The method according to claim 1, characterized in that: The integrating and processing the resources of each cloud microservice cluster based on the difference resources of each cloud microservice cluster to obtain a resource set with a unified format and protocol includes: Analyze the different resources of each cloud microservice cluster to determine the resources with inconsistent formats in each cloud microservice cluster; According to the preset template, the resources with inconsistent formats in each cloud microservice cluster are formatted and regularized to obtain a preliminary resource set with a unified format; Based on the interactive protocol specification, the preliminary resource set with unified format is subjected to protocol regularization processing to obtain a resource set with unified format and protocol.

4. The method according to claim 1, characterized in that: The coordinating and unifying the technical specifications of the existing microservice frameworks of the cloud microservice clusters to obtain the optimized service frameworks corresponding to the cloud microservice clusters includes: Collect technical specification information of existing microservice frameworks of each cloud microservice cluster, including historical connection data of service discovery and registration mechanisms, existing communication protocols, and existing security frameworks; Determine the docking problem of the service discovery and registration mechanism according to the historical docking data; and update the process of the service discovery and registration mechanism based on the docking problem to obtain an optimized process; Perform unified conversion and standardization on existing communication protocols to obtain optimized communication protocols; The existing security framework includes: at least one of an identity authentication method, an authorization mechanism, and a data encryption method, and the existing security framework is integrated to obtain an optimized security framework; By combining the optimization process, the optimized communication protocol and the optimized security framework, an optimized service framework corresponding to each cloud microservice cluster is obtained.

5. The method according to claim 1, characterized in that: Based on the optimized service framework, according to the overall business requirements of each cloud microservice cluster and the differentiated resources, the resources in the resource set are allocated in each cloud microservice cluster, including: Determine the demand trend information of the business demand for various resources according to the overall business demand of each cloud microservice cluster and the differentiated resources; Obtaining actual usage and remaining availability of resources in the resource set; Based on the optimized service framework, the resources in the resource set are allocated in each cloud microservice cluster according to the demand trend information and the actual usage and remaining availability of the resources in the resource set.

6. The method according to claim 1, characterized in that After allocating resources in the resource set among the cloud microservice clusters based on the optimized service framework and the overall business requirements of the cloud microservice clusters and the differential resources, the method further includes: Collecting operation status data of each cloud microservice cluster, wherein the operation status data includes: one or more of resource utilization, service response time, and operation stability; When it is determined based on the operating status data of each cloud microservice cluster that there is a resource anomaly or service failure, a corresponding solution is called to perform repair processing.

7. A cloud server, characterized in that: include: A heterogeneous cluster management module is used to determine the different resources of each cloud microservice cluster of the power grid in terms of resource characteristics, wherein the resource characteristics include: one or more of computing resources, storage resources, network resources, interface specifications, and management modes; based on the different resources of each cloud microservice cluster, the resources of each cloud microservice cluster are integrated to obtain a resource set with a unified format and protocol; A microservice framework consistency coordination module is used to coordinate and unify the technical specifications of the existing microservice frameworks of each cloud microservice cluster to obtain an optimized service framework corresponding to each cloud microservice cluster; The resource allocation optimization module is used to allocate the resources in the resource set among the cloud microservice clusters based on the optimized service framework and according to the overall business requirements of the cloud microservice clusters and the differential resources.

8. A cloud server, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: 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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