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

Abstract

The application relates to a cloud micro-service cluster management method, a cloud server, a storage medium and a computer program product. The method is applied to the cloud server and comprises the following steps: determining difference resources of each cloud micro-service cluster of a 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 difference resources of each cloud micro-service cluster, performing integration processing on the resources of each cloud micro-service cluster to obtain a resource set with unified format and protocol; performing coordinated and unified processing on the technical specifications of existing micro-service frameworks 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, performing resource allocation on the resources in the resource set in each cloud micro-service cluster according to overall business requirements and the difference resources of each cloud micro-service cluster.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grids, in particular to a cloud micro-service cluster management method, a cloud server, a storage medium and a computer program product. BACKGROUND

[0002] In the current heterogeneous cluster management field, the management of cloud micro-service clusters mainly relies on traditional decentralized management mode. Different architectures and technologies build clusters separately, and each cluster operates according to its own resource management strategy. Computing resources are allocated according to local load, storage resources are allocated according to pre-planned storage quotas, and network resources are dynamically adjusted according to local network traffic.

[0003] In terms of micro-service framework, different clusters select different frameworks according to their business characteristics. Some use frameworks based on Spring Cloud, focusing on rapid development and convenient deployment of services; others use Kubernetes-related micro-service architectures, focusing on containerized management and efficient resource utilization. In terms of service discovery and registration mechanism, different frameworks have different implementation methods. Some use DNS resolution to implement service positioning, and some use a dedicated registration center to register service information. Communication protocols are also diversified, including RESTful protocols based on HTTP for regular data interaction and gRPC protocols for high-performance and low-latency communication requirements. Security frameworks are also built according to their own security needs, covering various identity verification methods such as password verification and token verification. Authorization mechanisms are customized according to business roles and permission levels, and data encryption methods are selected according to the sensitivity of the data.

[0004] However, the existing cloud micro-service cluster management technology in the field of heterogeneous cluster management has problems such as uneven resource allocation, low scheduling efficiency, difficulty in unifying micro-service frameworks, non-standard communication protocols, and large security risks, which restrict the application and development of cloud micro-services. SUMMARY

[0005] Therefore, it is necessary to provide a cloud micro-service cluster management method, a cloud server, a storage medium and a computer program product to solve the above technical problems.

[0006] In a first aspect, the present application provides a cloud micro-service cluster management method applied to a cloud server, comprising:

[0007] determining the difference resources of each cloud micro-service cluster of the power grid in terms of resource characteristics, the resource characteristics including one or more of computing resources, storage resources, network resources, interface specifications, and management modes;

[0008] based on the difference resources of the cloud micro-service clusters, the resources of the cloud micro-service clusters are integrated to obtain a resource set with unified format and protocol;

[0009] The technical specifications of the existing micro-service frameworks of the cloud micro-service clusters are coordinated and unified to obtain the optimized service frameworks corresponding to the cloud micro-service clusters.

[0010] Based on the optimized service frameworks, the resources in the resource set are allocated in the cloud micro-service clusters according to the overall business requirements of the cloud micro-service clusters and the difference resources.

[0011] In one embodiment, the determination of the difference resources of the cloud micro-service clusters of the power grid in terms of resource characteristics includes:

[0012] The resource characteristics of the cloud micro-service clusters of the power grid are extracted to obtain resource characteristic information of the cloud micro-service clusters.

[0013] The resource characteristic information of the cloud micro-service clusters is compared and analyzed to determine the difference resources of the cloud micro-service clusters in terms of resource characteristics.

[0014] In one embodiment, based on the difference resources of the cloud micro-service clusters, the resources of the cloud micro-service clusters are integrated to obtain a resource set with unified format and protocol, including:

[0015] The difference resources of the cloud micro-service clusters are analyzed to determine the resources with inconsistent formats in the cloud micro-service clusters.

[0016] According to a preset template, the resources with inconsistent formats in the cloud micro-service clusters are processed to obtain a preliminary resource set with unified format.

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

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

[0019] The technical specification information of the existing micro-service frameworks of the cloud micro-service clusters is collected, and the technical specification information includes historical interface data of service discovery and registration mechanism, existing communication protocol and existing security framework.

[0020] According to the historical docking data, a docking problem of the service discovery and registration mechanism is determined, and a process of the service discovery and registration mechanism is updated based on the docking problem to obtain an optimized process;

[0021] The existing communication protocol is uniformly converted and standardized to obtain an optimized communication protocol.

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

[0023] In combination with the optimized process, the optimized communication protocol, and the optimized security framework, an optimized service framework corresponding to each cloud micro-service cluster is obtained.

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

[0025] According to the overall business requirements of the cloud micro-service clusters and the difference resources, demand tendency information of business requirements on various types of resources is determined.

[0026] Actual usage and remaining availability of resources in the resource set are obtained.

[0027] Based on the optimized service framework, resources in the resource set are allocated in the cloud micro-service clusters according to the demand tendency information and the actual usage and remaining availability of resources in the resource set.

[0028] In one embodiment, based on the optimized service framework, after resources in the resource set are allocated in each cloud micro-service cluster according to overall business requirements of the cloud micro-service clusters and the difference resources, the method further includes:

[0029] Running state data of the cloud micro-service clusters is collected, and the running state data includes one or more of resource utilization, service response time, and running stability.

[0030] In a case where it is determined according to the running state data of the cloud micro-service clusters that there is a resource exception or a service failure, a corresponding solution is called to repair.

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

[0032] The isomer cluster management module is configured to determine difference resources of each cloud micro-service cluster of the power grid in resource characteristics, the resource characteristics including one or more of computing resources, storage resources, network resources, interface specifications, and management modes; and perform integration processing on resources of the each cloud micro-service cluster based on the difference resources of the each cloud micro-service cluster, to obtain a resource set with unified format and protocol.

[0033] The micro-service framework consistency coordination module is configured to perform unified processing on technical specifications of existing micro-service frameworks of the each cloud micro-service cluster, to obtain an optimized service framework corresponding to the each cloud micro-service cluster.

[0034] The resource allocation optimization module is configured to perform resource allocation of resources in the resource set in the each cloud micro-service cluster based on the optimized service framework, according to overall business requirements of the each cloud micro-service cluster and the difference resources.

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

[0036] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method in the first aspect.

[0037] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and the computer program is executed by a processor to implement the method in the first aspect.

[0038] The cloud micro-service cluster management method is applied to a cloud server, and can determine the difference resources of each cloud micro-service cluster of a power grid in terms of resource characteristics, the resource characteristics including one or more of computing resources, storage resources, network resources, interface specifications, and management modes; based on the difference resources of each cloud micro-service cluster, the resources of each cloud micro-service cluster are integrated to obtain a resource set with unified format and protocol; the technical specifications of the existing micro-service frameworks of each cloud micro-service cluster are coordinated and unified to obtain an optimized service framework corresponding to each cloud micro-service cluster; and based on the optimized service framework, the resources in the resource set are allocated in each cloud micro-service cluster according to the overall business requirements of each cloud micro-service cluster and the difference resources. According to the scheme, the difference resources of each cloud micro-service cluster of the power grid in terms of multiple resource characteristics are accurately determined, and then the resources are integrated to obtain a set with unified format and protocol, so that the problem of unbalanced resource allocation is solved; the technical specifications of the existing micro-service frameworks are coordinated and unified to form an optimized service framework, so that the difficulty of unifying the micro-service frameworks is overcome, the standardization of the communication protocol is improved, and the obstacle of the non-standard communication protocol is eliminated; the resources are allocated on demand based on the optimized framework, the scheduling efficiency is greatly improved, and the security of the framework is strengthened to avoid the risk of security hazards, so that the wide and stable application and vigorous development of cloud micro-services in various industries are promoted in all directions. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0040] Figure 1 A flowchart of a cloud micro-service cluster management method in an embodiment;

[0041] Figure 2 A flowchart of another cloud micro-service cluster management method in an embodiment;

[0042] Figure 3 A flowchart of still another cloud micro-service cluster management method in an embodiment;

[0043] Figure 4 A structural block diagram of a cloud server in an embodiment;

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

[0045] Figure 6A structural block diagram of the micro-service framework consistency coordination module 402 in an embodiment;

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

[0047] Figure 8 A structural block diagram of the scheduling coordination module 405 in an embodiment;

[0048] Figure 9 A structural block diagram of the unified supervision module 404 in an embodiment;

[0049] Figure 10 An internal structural diagram of a cloud server in an embodiment. DETAILED DESCRIPTION

[0050] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0051] With the vigorous development of cloud computing and micro-service, a large number of enterprises have joined in the application exploration of cloud micro-service. However, in the key field of cloud micro-service cluster integration and management, the current technology used exposes many problems that need to be overcome.

[0052] First, focus on the management of heterogeneous clusters. So-called "heterogeneous clusters" are clusters composed of different architectures and various technologies. These heterogeneous clusters differ significantly in several key aspects: first, the resource characteristics differ significantly, covering computing resources (such as the CPU computing power of servers, GPU parallel computing capabilities, etc. for data processing capabilities), storage resources (such as hard disk storage capacity, storage read / write speed, etc. for data storage resources), network resources (such as network bandwidth, network delay, etc. to determine the speed and smoothness of data transmission); second, the interface specifications are different, which involves the interface standards followed when different clusters are connected and interacted with each other; third, the management modes are different, and the resource allocation, task scheduling and other management means used by each cluster are quite different.

[0053] As a result, a series of problems are triggered. On the one hand, the situation of uneven resource allocation often occurs. Due to the lack of an optimal allocation strategy that can coordinate the overall business demand, some areas are in urgent need of resources due to business concentration, while other areas have a large amount of idle resources due to the lack of business, resulting in serious waste. For example, when dealing with some key business tasks that require a large amount of computing resources, the computing resources required by the key business cannot be satisfied due to the lack of overall planning of computing resource allocation, which further delays the business process and destroys the smoothness and timeliness of the overall business process. On the other hand, in the scheduling link, due to the lack of unified coordination mechanism, it is difficult to respond quickly and efficiently in the face of dynamic changes in business, and the scheduling efficiency is extremely low. Furthermore, at the regulatory level, it is difficult to implement an effective unified regulatory strategy due to the huge differences between clusters, and many potential problems cannot be detected and solved in a timely manner.

[0054] Secondly, focusing on the consistency problem of micro-service framework. In a heterogeneous environment, the existing micro-service framework is difficult to achieve uniform technical specifications. Specifically, the service discovery and registration mechanism cannot be seamlessly connected in different operating environments, which is like people in different countries using different languages to communicate, which hinders the dynamic deployment and expansion of micro-services, making it difficult for new micro-services to quickly and flexibly integrate into the existing system; the communication protocol lacks a unified standard, like trains with different track gauges cannot run on the same track, the communication efficiency between services is low and the stability is poor, and the conversion between different protocols is difficult, which undoubtedly greatly increases the complexity of the system; the inconsistency of the security framework is more serious, the identity verification method (such as password, fingerprint, facial recognition, etc. means to confirm the identity of the user), authorization mechanism (rules set to specify which users can access which resources) and data encryption method (algorithm to convert data into ciphertext to protect data security) are different, which provides an opportunity for malicious attackers, not only increases the management cost and difficulty, but also greatly reduces the development efficiency, seriously hindering the stable development of cloud micro-services.

[0055] In summary, in view of the many defects of existing technologies in the fusion management of cloud micro-service clusters, there is an urgent need for a cloud micro-service cluster management method that can achieve efficient, stable and reliable cluster management goals, providing protection for the widespread application and healthy development of cloud micro-services in various industries.

[0056] To solve the above problems, the embodiment of the application provides a cloud micro-service cluster management and control method, which is applied to a cloud server. Through the method, the differences in resources of each cloud micro-service cluster of the power grid in various resource characteristics are accurately determined, and then the resources are integrated to obtain a set with unified format and protocol, so that the problem of uneven resource allocation is solved. The technical specifications of the existing micro-service framework are coordinated and unified to form an optimized service framework, so that the difficulty of unifying the micro-service framework is overcome, and the standardization of the communication protocol is improved, and the obstacle of the non-standard communication protocol is eliminated. Resources are allocated on demand based on the optimized framework, which greatly improves the scheduling efficiency. By strengthening the security of the framework, the risk of security hidden danger is avoided, and the wide and stable application and vigorous development of cloud micro-service in various industries are promoted.

[0057] In one exemplary embodiment, as shown in Figure 1 a flowchart of a cloud micro-service cluster management and control method is provided. The method can be executed by a cloud server, and a cloud micro-service cluster management and control platform can be configured in the cloud server. The steps of the method can be specifically executed by the cloud micro-service cluster management and control platform. The method comprises the following steps:

[0058] 101. Determine the difference in resources of each cloud micro-service cluster of the power grid in terms of resource characteristics.

[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 resources of each cloud micro-service cluster of the power grid in terms of resource characteristics comprises: extracting the resource characteristics of each cloud micro-service cluster of the power grid to obtain resource characteristic information of each cloud micro-service cluster; and comparing and analyzing the resource characteristic information of each cloud micro-service cluster to determine the difference in resources of each cloud micro-service cluster in terms of resource characteristics.

[0061] In one possible implementation, first, a special data collection tool is used to extract the resource characteristics of each cloud micro-service cluster of the power grid; then, a data analysis algorithm is used to compare and analyze the collected resource characteristic information of each cloud micro-service cluster.

[0062] The data collection tool can be a program developed based on software agent (Agent) technology, which is deployed on each cluster node and can collect information such as CPU usage rate, memory occupancy (belonging to the computing resource category), used space of hard disk, read / write speed (reflecting the storage resource situation), real-time bandwidth of network interface, data packet transmission delay (reflecting the network resource state), and interface specification description document for external interaction of each cluster, and internal resource management mode document, to obtain the resource characteristic information of each cloud micro-service cluster.

[0063] Among them, the data analysis algorithm covers a variety of methods for processing, analyzing and extracting valuable information from data. In the cloud micro-service cluster management scenario, the data analysis algorithm undertakes the key task. The massive resource characteristic information collected from the data collection tool, such as the CPU usage, memory occupation, storage read-write rate, network bandwidth data of each cluster node, as well as interface specification, management mode description and other text information, need to be sorted out through the data analysis algorithm. It can clean up these heterogeneous data, remove invalid, incorrect or duplicate data records, such as eliminating obviously abnormal CPU usage data points caused by sensor failure. It can also convert data to make data of different formats and different dimensions comparable, such as converting storage data in bytes to megabytes. Through these preprocessing operations, a foundation is laid for subsequent accurate analysis to fully understand the running state and resource distribution of each cloud micro-service cluster.

[0064] For example, using a clustering analysis algorithm based on data mining, clusters with similar resource characteristics are classified into a class, and by comparing key indicators between different classes, such as the mean difference of CPU core number between different clusters, the standard deviation of storage capacity, etc., the difference resources of each cloud micro-service cluster in resource characteristics are accurately determined. This analysis helps to subsequently target resource allocation and management.

[0065] The above clustering analysis algorithm based on data mining, its core principle is to divide the data set into different clusters or classes according to the similarity between data objects. When determining the resource characteristic differences of the power grid cloud micro-service cluster, some key features will be selected as the basis for clustering, such as CPU core number and main frequency for computing resources, storage capacity and read-write speed for storage resources, bandwidth and delay for network resources, etc. The algorithm calculates the distance between these features, such as Euclidean distance, Manhattan distance, etc. Taking Euclidean distance as an example, if the numerical difference between two clusters in CPU core number, storage capacity and other dimensions is small, the Euclidean distance between them is short, meaning high similarity, and they will be classified into the same cluster. Through continuous iteration of this process, clusters with similar resource characteristics are finally gathered together to form different categories. Once the clustering is completed, by comparing the mean, standard deviation and other statistics of key indicators between different clusters, the difference resources of each cloud micro-service cluster in resource characteristics can be clearly found. For example, a cluster has fewer average CPU cores but larger storage capacity, while another cluster may be the opposite, which provides a precise direction for subsequent targeted resource allocation and optimization of cluster configuration, avoiding unreasonable allocation and waste of resources, and improving the running efficiency of the entire cloud micro-service cluster system.

[0066] In another possible implementation: a centralized management and control platform (such as the management and 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 characteristic query instructions to each cluster, and each cluster receives the instructions and feeds back its resource characteristic data in a preset data format, which covers various types of resource information mentioned above. After receiving the data of all clusters, the management and control platform uses the built-in comparison module to present the numerical comparison of each cluster in each dimension of resource characteristics in tabular form, and then determines the difference resources. The "comparison module" here can be a program developed based on a Python (a widely used high-level programming language) data analysis library (such as the Pandas library), which can efficiently handle large-scale data comparison tasks.

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

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

[0069] In one possible implementation: the difference resources of each cloud microservice cluster are analyzed in depth, and the resources with inconsistent formats in each cloud microservice cluster are determined through feature recognition of resource format. For example, for data files in storage resources, some clusters store data records in Comma-Separated Values (CSV) format, and some use JavaScript Object Notation (JSON) to represent objects.

[0070]

[0071] ​JSON) format, the format differences are identified by file header identification, data structure characteristics, etc. For the identified inconsistent formats, the format is normalized according to the preset template. The "preset template" here can be a pre-defined general data format standard, for example, it is stipulated to uniformly use eXtensible Markup Language (XML) format to store structured data, and a special format conversion tool is developed to convert the data in CSV or JSON format according to the syntax rules of XML, so as to obtain a preliminary resource set with uniform format.

[0072] In another possible implementation, by means of an automated resource integration software platform, the platform integrates a format identification engine, a template matching module and a protocol adaptation component. When the difference resources of each cloud micro-service cluster are input, the format identification engine quickly identifies the format types of different resources, such as the format of text resources and the format of binary resources, by using pattern recognition algorithms in machine learning. The template matching module selects the most suitable template to convert the resources according to the built-in multiple standard format templates, and obtains an intermediate format-unified resource set. Finally, the protocol adaptation component adapts the resources in the intermediate resource set according to the pre-configured interaction 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 uniform format and protocol, so as to facilitate subsequent unified management and deployment.

[0073] 103. Coordinating and unifying the technical specifications of the existing micro-service frameworks of each cloud micro-service cluster to obtain the corresponding optimized service framework of each cloud micro-service cluster.

[0074] In some embodiments, as shown in Figure 2 Another flowchart of a method for managing and controlling a cloud micro-service cluster is provided, which coordinates and unifies the technical specifications of the existing micro-service frameworks of each cloud micro-service cluster to obtain the corresponding optimized service framework of each cloud micro-service cluster, including the following steps:

[0075] 103a. Collecting technical specification information of the existing micro-service frameworks of each cloud micro-service cluster, including historical interface data of service discovery and registration mechanism, existing communication protocols and existing security frameworks.

[0076] In a possible implementation, the technical specification information of the existing microservice framework of each cloud microservice cluster is collected by using a special information collection tool, such as a web crawler technology combined with API calls. The historical interfacing data of the service discovery and registration mechanism are obtained by accessing the operation and maintenance log databases of each cluster to extract the timestamp, success or failure of interfacing, error code, and other information in the past service interfacing process. The existing communication protocol is read from the configuration file of each cluster to obtain the protocol type, port number, data transmission format, and other parameters. The existing security framework is parsed to obtain the identity authentication method (such as the common username / password authentication and authentication based on digital certificates), authorization mechanism (role-based access control or attribute-based access control), and data encryption method.

[0077] 103b. According to the historical interfacing data, the interfacing problem of the service discovery and registration mechanism is determined, and the process of the service discovery and registration mechanism is updated based on the interfacing problem to obtain an optimized process.

[0078] In a possible implementation, the interfacing problem of the service discovery and registration mechanism is determined according to the collected historical interfacing data by manual analysis or with the help of a fault diagnosis algorithm. For example, if a large number of records of interfacing failure are concentrated in a certain time period, and the error code points to a network timeout problem, it may mean that the network configuration has defects. The process of the service discovery and registration mechanism is updated based on the interfacing problem, such as optimizing the network configuration parameters and adjusting the service heartbeat detection interval, to obtain an optimized process.

[0079] 103c. The existing communication protocol is uniformly converted and processed to obtain an optimized communication protocol.

[0080] In a possible implementation, the existing communication protocol is uniformly converted and processed by using a protocol conversion middleware. The middleware has built-in conversion rules for a plurality of common communication protocols. For example, when converting a simple communication protocol based on UDP into a reliable communication protocol based on TCP, the data packet is re-encapsulated and transmission control is performed according to the three-way handshake and data confirmation mechanisms of the TCP protocol, to ensure the reliability of data transmission, and an optimized communication protocol is obtained.

[0081] 103d. The existing security framework is uniformly integrated 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 a possible implementation, for the unified integration of the existing security framework, a security framework fusion tool is developed. If one cluster adopts username / password verification and another cluster adopts digital certificate verification, the fusion tool can uniformly access the username / password verification entry in the front end, and map the user information verified by the username / password verification to the digital certificate in the back end, to realize the uniformity of the identity verification mode; for the authorization mechanism, different rules are sorted out, common rules are extracted, and a unified authorization decision engine is constructed; for the data encryption mode, a high-strength and compatible encryption algorithm is selected as the standard, and the data that needs to be encrypted is uniformly processed by using the algorithm to obtain the optimized security framework.

[0084] 103e, in combination with the optimization process, the optimized communication protocol and the optimized security framework, the optimized service framework corresponding to each cloud micro-service cluster is obtained.

[0085] In a possible implementation, the optimization process, the optimized communication protocol and the optimized security framework are organically combined, and embedded into the micro-service framework corresponding to each cloud micro-service cluster through configuration file updating, code module replacement and the like, to obtain the optimized service framework corresponding to each cloud micro-service cluster.

[0086] In a possible implementation, a cloud micro-service framework management and control center is deployed, which establishes a bidirectional communication link with each cloud micro-service cluster, collects the micro-service framework technical specification information of each cluster by sending standardized information collection instructions. The management and control center is built-in with an intelligent analysis engine, which uses big data analysis technology to deeply mine historical docking data, automatically discovers potential docking problems of the service discovery and registration mechanism, such as frequent repeated registration, which may be due to the imperfect deduplication mechanism of the registration center, and then generates targeted optimization process suggestions, which are manually adjusted by the operation and maintenance personnel or executed by automatic scripts. The elastic computing resources provided by the cloud computing platform are used to run the protocol unification conversion service. The service is deployed based on containerization technology, and each container is responsible for the conversion task of one 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, to efficiently obtain the optimized communication protocol. For the unified integration of the security framework, a distributed security trust system is built using blockchain technology. The identity verification, authorization mechanism and data encryption information of each cluster are chained, and the consensus mechanism of the blockchain is used to ensure the consistency and tamper resistance of the information. The intelligent contract is developed on the chain to realize mutual recognition of identity verification, unified coordination of authorization mechanism and standardization of data encryption, to obtain the optimized security framework. Finally, the management and control center distributes the generated optimized service framework to each cloud micro-service cluster in the form of a software package, and each cluster realizes the rapid updating and replacement of the framework through automatic deployment tools to obtain the corresponding optimized service framework.

[0087] 104. Based on the optimized service framework, resources in the resource set are allocated to the cloud micro-service clusters according to the overall business requirements and different resources of the cloud micro-service clusters.

[0088] In some embodiments, as shown in Figure 3 Another flowchart of a method for managing and controlling a cloud micro-service cluster is provided, which is based on the optimized service framework and allocates resources in the resource set to the cloud micro-service clusters according to the overall business requirements and different resources of the cloud micro-service clusters, including the following steps:

[0089] 104a. Determine the demand tendency information of the business requirements for various resources according to the overall business requirements and different resources of the cloud micro-service clusters.

[0090] In one possible implementation, a business requirement modeling tool is used for modeling analysis according to the overall business requirements and different resources of the cloud micro-service clusters. For example, for a power grid cloud micro-service cluster that includes power load forecasting and power equipment monitoring, the demand tendency information of the business requirements for various resources is determined by analyzing business processes, data flow, task priority, and other factors. For example, the power load forecasting business has a high demand for GPU computing power in the computing resources because it involves a large amount of parallel computing of data. The power equipment monitoring business has a high real-time requirement for network resources, which requires low-latency network transmission to ensure timely data feedback.

[0091] In one possible implementation, an intelligent resource allocation platform is constructed, which is deeply integrated with the optimized service framework and receives real-time business requirement change information of the cloud micro-service clusters. Deep learning algorithms in artificial intelligence are used to perform correlation analysis on the business requirements and different resources, automatically learn the demand patterns of various resources in different business scenarios, and thus determine the demand tendency information of the business requirements for various resources.

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

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

[0094] In a possible implementation, the actual usage and the remaining availability of the resources in the resource set are directly obtained by means of the resource management API of the cloud computing platform, without the need to additionally build a monitoring system. These APIs can provide high-precision resource data and have the characteristics of strong real-time performance and convenient invocation.

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

[0096] In a possible implementation, based on the above-optimized service framework, the resources in the resource set are allocated in each cloud micro-service cluster by using a resource allocation algorithm according to the demand tendency information and the actual usage and the remaining availability of the resources in the resource set. For example, a resource allocation strategy based on a greedy algorithm is adopted, the cluster with high business demand tendency and sufficient resource remaining availability is preferentially satisfied, and the resource allocation scheme is gradually optimized to guarantee the efficient operation of the overall business.

[0097] In a possible implementation, a resource allocation model based on reinforcement learning is run on the above-constructed intelligent resource allocation platform. The model regards the resource allocation process as a Markov decision process, and dynamically adjusts the resource allocation strategy according to the demand tendency information and the actual usage and the remaining availability of the resources in the resource set by constantly trying and learning, so as to optimally allocate the resources in the resource set in each cloud micro-service cluster to adapt to the changing business demands.

[0098] The above-mentioned cloud micro-service cluster management method is applied to a cloud server, the differences of the resources of each cloud micro-service cluster of the power grid in various resource characteristics are accurately determined, and then the resources are integrated to obtain a set with unified format and protocol, thereby solving the problem of unbalanced resource allocation; the technical specifications of the existing micro-service framework are coordinated and unified to form an optimized service framework, thereby overcoming the difficulty of unifying the micro-service framework, and improving the standardization of the communication protocol and eliminating the obstacle of non-standard communication protocol; the resources are allocated on demand based on the optimized framework, which greatly improves the scheduling efficiency, and the security of the reinforcement framework is also enhanced to avoid the risk of security hidden danger, thereby promoting the wide and stable application and vigorous development of cloud micro-services in various industries.

[0099] In some embodiments, based on the optimized service framework, according to the overall business requirements and different resources of each cloud micro-service cluster, after the resource allocation of the resources in the resource set in each cloud micro-service cluster, the cloud micro-service cluster management method provided by the embodiment of the application further comprises: collecting the running state data of each cloud micro-service cluster, the running state data comprising one or more of resource utilization, service response time, and running stability; in the case where it is determined that there is a resource exception or a service failure according to the running state data of each cloud micro-service cluster, a corresponding solution measure is called to repair.

[0100] The above-mentioned case of resource exception can include but is not limited to:

[0101] CPU resource exception: when the CPU usage rate is maintained at a high level for a long time, such as continuously higher than 90%, and there is no sudden large-scale parallel task demand in the system, it is an exception. This indicates that the CPU resource allocation is unreasonable or there is an excessive occupation program, so that the CPU continuously operates at high load and has no time to process new tasks, causing task queuing and system response delay.

[0102] Memory resource exception: if the memory occupancy rate continues to rise and frequently touches the system preset memory warning threshold, or even causes the system to start using virtual memory for data exchange, it is a typical memory resource exception. It is usually due to memory leakage problem, that is, the program applies memory but does not release it correctly, or some services excessively occupy memory, causing the available memory to approach zero, which may cause program crash at any time.

[0103] Disk input / output (I / O) resource exception: long-time low read-write rate, such as continuously lower than 50% of the normal read-write speed, is likely to be a hardware problem such as disk bad track and head failure, causing difficulty in data reading; and high read-write rate, for example, continuously more than 2 times of the normal peak value, and not caused by reasonable data batch processing demand, with a high probability of existence of unreasonable frequent read-write, such as program trapped in a dead loop for invalid read-write, occupying a large amount of system resources and slowing down the performance of the entire cluster.

[0104] The above-mentioned case of service failure can include but is not limited to:

[0105] Response time failure: The service has a clear response time requirement under the established business rules, such as the e-commerce platform order service needs to respond within 1 second. When the timing code segment in the key link records multiple times that the response time far exceeds this threshold, such as multiple delays of 3 seconds or more, it means that the service has failed. The reasons are that the associated backend service response is slow, such as database query efficiency drops due to index invalidation, lock table problems, or network transmission appears high delay due to insufficient bandwidth, routing failure, etc. It may also be that the service itself has business logic defects, and the algorithm complexity is too high to cause slow execution.

[0106] Running stability failure: Frequent monitoring of cluster node process unexpected termination and restart, such as more than 5 times of process restart of a service within 10 minutes, which basically determines that the service has a running stability failure. This is usually caused by internal program vulnerabilities, such as null pointer exceptions, array out-of-bounds errors, and other unhandled errors, or memory overflow, resource competition, which causes the process to be unable to run stably; in terms of network connection, if the number of interruptions per minute continues to exceed 10 times, far exceeding the normal network jitter allowed range, it will cause frequent interruptions of service communication, data transmission blocked, and services unable to work together, which also belongs to the service failure category.

[0107] In the above process of collecting the running state 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 earlier, but they focus more on real-time running state capture. For example, collecting detailed usage of resources such as CPU, memory, and disk I / O, and accurately calculating resource utilization. For service response time collection, a special timing code segment can be embedded in the key link of service call. When a service request is initiated and responded to, this code automatically records the request initiation time and response return time, and the difference between the two is the service response time. The collection of running stability data can be achieved by monitoring the process state of the cluster node and the number of network connection interruptions to comprehensively judge.

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

[0109] In some embodiments, a centralized data collection platform can also be adopted, which establishes efficient network connections such as HTTP / 2 with each cluster, and periodically sends data collection instructions to the cluster. The cluster feeds back resource utilization, service response time, running stability and other related data according to the predetermined data format. Such a platform usually has certain data caching and preprocessing capabilities, and can preliminarily clean the collected data to remove obvious outliers, such as resource utilization data points with extremely high errors caused by temporary network fluctuations. The collected data can use encryption protocols to ensure the confidentiality and integrity of the data during transmission, preventing data from being stolen or tampered with. For the collected data, a distributed database can be selected for data storage, which has high scalability and good read-write performance, and is suitable for storing large-scale cluster running state data. The data is stored in time series, which facilitates subsequent query and analysis.

[0110] The cloud micro-service cluster management method provided by the embodiments of the present application can accurately determine whether the resources are abnormal and whether the services are faulty according to the collected data, and can call corresponding solutions at the first time when the problem occurs. The cloud micro-service cluster is stable and efficient.

[0111] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0112] Based on the same inventive concept, the embodiments of the present application also provide a cloud server for implementing the cloud micro-service cluster management method described above. The implementation scheme for solving the problem provided by the cloud server is similar to the implementation scheme described in the above method, so the specific limitations in one or more cloud server embodiments provided below can refer to the limitations of the cloud micro-service cluster management method described above, and will not be repeated here.

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

[0114] The heterogeneous cluster management module 401 is configured to determine the difference resources of the cloud micro-service clusters of the power grid in terms of resource characteristics, the resource characteristics including one or more of the following: computing resources, storage resources, network resources, interface specifications, and management modes; and perform integration processing on the resources of the cloud micro-service clusters based on the difference resources of the cloud micro-service clusters, to obtain a resource set with unified format and protocol.

[0115] The micro-service framework consistency coordination module 402 is configured to perform unified processing on the technical specifications of the existing micro-service frameworks of the cloud micro-service clusters, to obtain the optimized service frameworks corresponding to the cloud micro-service clusters.

[0116] The resource allocation optimization module 403 is configured to perform resource allocation of the resources in the resource set in the cloud micro-service clusters based on the overall business requirements of the cloud micro-service clusters and the difference resources, according to the optimized service frameworks.

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

[0118] extract the resource characteristics of the cloud micro-service clusters of the power grid, to obtain resource characteristic information of the cloud micro-service clusters;

[0119] perform comparative analysis on the resource characteristic information of the cloud micro-service clusters, to determine the difference resources of the cloud micro-service clusters in terms of resource characteristics.

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

[0121] analyze the difference resources of the cloud micro-service clusters, to determine the resources with inconsistent formats in the cloud micro-service clusters;

[0122] perform format normalization processing on the resources with inconsistent formats in the cloud micro-service clusters according to a preset template, to obtain a preliminary resource set with unified format;

[0123] perform protocol normalization processing on the preliminary resource set with unified format based on an interaction protocol specification, to obtain a resource set with unified format and protocol.

[0124] In some embodiments, the micro-service framework consistency coordination module 402 is specifically configured to:

[0125] collect technical specification information of the existing micro-service frameworks of the cloud micro-service clusters, the technical specification information including historical interfacing data of a service discovery and registration mechanism, an existing communication protocol, and an existing security framework;

[0126] According to the historical docking data, a docking problem of the service discovery and registration mechanism is determined, and a process of the service discovery and registration mechanism is updated based on the docking problem to obtain an optimized process.

[0127] The existing communication protocol is uniformly converted and standardized to obtain an optimized communication protocol.

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

[0129] In combination with the optimized process, the optimized communication protocol and the optimized security framework, an optimized service framework corresponding to the cloud micro-service clusters is obtained.

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

[0131] According to the overall business demand of the cloud micro-service clusters and the difference resources, demand tendency information of the business demand on various resources is determined.

[0132] Actual usage and remaining availability of resources in the resource set are obtained.

[0133] Based on the above-mentioned optimized service framework, according to the demand tendency information and the actual usage and remaining availability of resources in the resource set, the resources in the resource set are allocated in the cloud micro-service clusters.

[0134] In some embodiments, the cloud server further includes a unified supervision module 404 configured to collect running state data of the cloud micro-service clusters, the running state data including one or more of resource utilization, service response time and running stability.

[0135] In a case where it is determined according to the running state data of the cloud micro-service clusters that there is a resource exception or a service failure, a corresponding solution measure 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 between the cloud micro-service clusters, discover and solve potential problems in time, and ensure stable operation of the cloud micro-service clusters.

[0137] In some embodiments, the cloud server can further include a scheduling coordination module 405 configured to establish a unified coordination mechanism to improve scheduling efficiency, quickly respond to business changes, and ensure timeliness and effectiveness of scheduling between the cloud micro-service clusters.

[0138] For example, as shown in Figure 5, a structural block diagram of a heterogeneous cluster management module 401 is presented. Figure 5 The heterogeneous cluster management module 401 includes:

[0139] The submodules are: Difference Analysis Submodule 4011, Resource Integration Submodule 4012, and Interface Adaptation Submodule 4013, among which:

[0140] The difference analysis submodule 4011 performs a detailed analysis of the resource characteristics, interface specifications, and management modes of different cloud microservice clusters, identifies the differences, and transmits the analysis results to other relevant submodules.

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

[0142] The interface adaptation submodule 4013 constructs an adaptation mechanism to address the differences in interface specifications among different cloud microservice clusters, ensuring that data interaction and collaborative management can be achieved between different cloud microservice clusters.

[0143] For example, Figure 6 shows a structural block diagram of a consistency coordination module 402 in a microservice framework. Figure 6 The consistency coordination module 402 of the microservice framework includes: service interface submodule 4021, protocol specification submodule 4022, and security unification submodule 4023, wherein:

[0144] The service integration submodule 4021 is responsible for handling the integration of service discovery and registration mechanisms under 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 inter-service communication, solve the problem of difficulty in converting different protocols, and reduce system complexity.

[0146] The security unification submodule 4023 is used to unify authentication, authorization mechanisms and data encryption methods, eliminate security risks caused by inconsistent security frameworks, and improve system security.

[0147] For example, such as Figure 7 The diagram shown 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 assessment submodule 4032, and an allocation strategy generation submodule 4033, wherein:

[0148] The demand analysis submodule 4031 is configured to collect and analyze overall business demands, and determine demands of various businesses on computing resources, storage resources, network resources, and the like.

[0149] The resource evaluation submodule 4032 is configured to evaluate existing resources of each cluster, and master actual usage and remaining availability of the resources.

[0150] The allocation strategy generation submodule 4033 is configured to generate a reasonable resource allocation strategy in combination with results of demand analysis and resource evaluation, and realize balanced allocation of resources among the clusters.

[0151] As shown in FIG. 8, an exemplary structure block diagram of a scheduling coordination module 405 is shown, Figure 8 The scheduling coordination module 405 includes a business awareness submodule 4051, a coordination mechanism submodule 4052, and a response execution submodule 4053, wherein:

[0152] The business awareness submodule 4051 monitors business changes in real time, acquires demand information of the business on resource scheduling in a timely manner, and delivers the information to the coordination mechanism submodule 4052.

[0153] The coordination mechanism submodule 4052 starts a unified coordination mechanism based on the demand information of the business, and plans a reasonable scheduling scheme.

[0154] The response execution submodule 4053 executes scheduling operations in a timely manner according to the scheduling scheme formulated by the coordination mechanism submodule 4052, and guarantees smooth development of the business.

[0155] As shown in FIG. 9, an exemplary structure block diagram of a unified supervision module 404 is shown, Figure 9 The unified supervision module 404 includes:

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

[0157] The policy formulation submodule 4041 formulates a comprehensive and effective unified supervision policy by comprehensively considering characteristics and commonalities of various cloud microservice clusters, and clearly defines focuses and rules of supervision.

[0158] The problem monitoring submodule 4042 monitors running states of various cloud microservice clusters in real time according to the formulated supervision policy, discovers potential problems such as resource abnormalities and service failures in a timely manner, and delivers problem information to the processing execution submodule 4043.

[0159] The processing execution submodule 4043 takes corresponding solving measures in a timely manner for problems discovered by the problem monitoring submodule 4042, and guarantees stable running of the various cloud microservice clusters.

[0160] The modules in the cloud server can be implemented by software, hardware, or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in the computer device in software form, so that the processor can call and execute the operations corresponding to the modules.

[0161] In an example embodiment, a cloud server is provided, and an internal structure diagram of the cloud server can be as shown in Figure 10 The computer device includes a processor, a memory, an input / output interface, and a communication interface. 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. The processor of the computer device is configured 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 running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store data. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a cloud micro-service cluster management method.

[0162] Those skilled in the art can understand that Figure 10 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can 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 example embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the cloud micro-service cluster management method provided in the method embodiments.

[0164] In an embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the cloud micro-service cluster management method provided in the method embodiments.

[0165] In an embodiment, a computer program product is provided, including a computer program. The computer program is executed by a processor to implement the cloud micro-service cluster management method provided in the method embodiments.

[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. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present 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 storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric 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 but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0167] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0168] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for managing and controlling a cloud micro-service cluster, characterized in that, Applied to a cloud server, comprising: Determine the difference resources of each cloud micro-service cluster of the power grid in resource characteristics, the resource characteristics including: one or more of computing resources, storage resources, network resources, interface specifications, management modes; Based on the difference resources of each cloud micro-service cluster, the resources of each cloud micro-service cluster are integrated to obtain a resource set with unified format and protocol; The technical specifications of the existing micro-service framework of each cloud micro-service cluster are coordinated and unified to obtain the optimized service framework corresponding to each cloud micro-service cluster; Based on the optimized service framework, according to the overall business demand of each cloud micro-service cluster and the difference resources, the resources in the resource set are allocated in each cloud micro-service cluster; The difference resources of each cloud micro-service cluster are analyzed to determine the inconsistent format resources in each cloud micro-service cluster; the inconsistent format resources in each cloud micro-service cluster are processed according to a preset template to obtain a preliminary resource set with unified format; and the preliminary resource set with unified format is processed according to an interaction protocol specification to obtain a resource set with unified format and protocol; The technical specifications of the existing micro-service framework of each cloud micro-service cluster are coordinated and unified to obtain the optimized service framework corresponding to each cloud micro-service cluster, including: collecting technical specification information of the existing micro-service framework of each cloud micro-service cluster, the technical specification information including: historical interface data of service discovery and registration mechanism, existing communication protocol and existing security framework; determining the interface problem of the service discovery and registration mechanism according to the historical interface data; and updating the process of the service discovery and registration mechanism based on the interface problem to obtain an optimized process; uniformly converting and processing the existing communication protocol to obtain an optimized communication protocol; the existing security framework includes at least one of identity verification method, authorization mechanism and data encryption method, and the existing security framework is uniformly integrated to obtain an optimized security framework; combining the optimized process, the optimized communication protocol and the optimized security framework, the optimized service framework corresponding to each cloud micro-service cluster is obtained. The difference resources of each cloud micro-service cluster of the power grid in resource characteristics, including:

2. The method of claim 1, wherein, Data extraction is performed on the resource characteristics of each cloud micro-service cluster of the power grid to obtain resource characteristic information of each cloud micro-service cluster; The resource characteristic information of each cloud micro-service cluster is compared and analyzed to determine the difference resources of each cloud micro-service cluster in resource characteristics. Based on the above optimized service framework, according to the overall business demand of each cloud micro-service cluster and the difference resources, the resources in the resource set are allocated in each cloud micro-service cluster, including:

3. The method of claim 1, wherein, ​ According to the overall business requirements of each cloud micro-service cluster and the difference resources, determine the demand tendency information of business requirements on each type of resource; Obtain the actual usage and remaining availability of the resources in the resource set; Based on the above optimized service framework, according to the demand tendency information and the actual usage and remaining availability of the resources in the resource set, allocate the resources in the resource set in each cloud micro-service cluster.

4. The method of claim 1, wherein, After the resource allocation of the resources in the resource set in each cloud micro-service cluster based on the optimized service framework and the overall business requirements of each cloud micro-service cluster and the difference resources, the method further comprises: Collect the running state data of each cloud micro-service cluster, which includes one or more of resource utilization, service response time, and running stability; In the case where it is determined that there is a resource exception or service failure according to the running state data of each cloud micro-service cluster, call the corresponding solution measures for repair processing.

5. A cloud server, characterized by, Comprise: The heterogeneous cluster management module is used to determine the difference resources of each cloud micro-service cluster of the power grid in terms of resource characteristics, which include one or more of computing resources, storage resources, network resources, interface specifications, and management modes; based on the difference resources of each cloud micro-service cluster, integrate the resources of each cloud micro-service cluster to obtain a resource set with unified format and protocol; The micro-service framework consistency coordination module is used to coordinate and unify the technical specifications of the existing micro-service frameworks of each cloud micro-service cluster to obtain the optimized service framework corresponding to each cloud micro-service cluster; The resource allocation optimization module is used to allocate the resources in the resource set in each cloud micro-service cluster based on the optimized service framework and the overall business requirements of each cloud micro-service cluster and the difference resources; Based on the difference resources of each cloud micro-service cluster, the resource integration processing of the resources of each cloud micro-service cluster is performed to obtain a resource set with unified format and protocol, which comprises: Analyze the difference resources of each cloud micro-service cluster to determine the inconsistent format resources in each cloud micro-service cluster; according to a preset template, perform format regularization processing on the inconsistent format resources in each cloud micro-service cluster to obtain a preliminary resource set with unified format; based on an interaction protocol specification, perform protocol regularization processing on the preliminary resource set with unified format to obtain a resource set with unified format and protocol; The technical specifications of the existing microservice frameworks of the cloud microservice clusters are coordinated and uniformly processed to obtain the optimized service frameworks corresponding to the cloud microservice clusters, including: collecting technical specification information of the existing microservice frameworks of the cloud microservice clusters, the technical specification information including: historical interfacing data of a service discovery and registration mechanism, an existing communication protocol, and an existing security framework; determining an interfacing problem of the service discovery and registration mechanism according to the historical interfacing data; and updating a process of the service discovery and registration mechanism based on the interfacing problem to obtain an optimized process; uniformly converting and standardizing the existing communication protocol to obtain an optimized communication protocol; the existing security framework including: at least one of an identity verification mode, an authorization mechanism, and a data encryption mode, and the existing security framework is uniformly integrated to obtain an optimized security framework; and combining the optimized process, the optimized communication protocol, and the optimized security framework to obtain the optimized service frameworks corresponding to the cloud microservice clusters. 6.A cloud server, comprising a memory and a processor, wherein the memory stores a computer program, and the cloud server is characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4.

8. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4. The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4.

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