Power grid heterogeneous cluster resource management method, cloud server, storage medium and computer program product

Abstract

The invention relates to a power grid heterogeneous cluster resource management method, a cloud server, a storage medium and a computer program product. The method is applied to a cloud server, and comprises the following steps: discovering and integrating resources in a heterogeneous cluster of a power grid to determine centralized integrated resources; acquiring operation parameters of a software system and state information of hardware equipment in the power grid heterogeneous cluster; determining configuration parameters of the cloud service based on the operation parameters of the software system and the state information of the hardware equipment; and carrying out resource allocation on the centralized integration resources based on the service requirements and the configuration parameters of the cloud service. By adopting the method, the operation stability and reliability of the whole power grid cloud micro-service platform can be optimized.

Description

Technical Field

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

[0002] With the acceleration of the digitalization process of the power grid, the power grid cloud microservice platform came into being. At the technical level, it covers heterogeneous cluster resource management and cloud service-related technologies, aiming to provide support for power grid business. Heterogeneous cluster resources are distributed in different computer rooms, each with certain resource configuration and management methods. At the same time, the existing microservice framework also has mechanisms such as service discovery, registration, configuration management and load balancing to achieve interaction and collaborative work between microservices, and to a certain extent has the function of automated operation and maintenance, trying to monitor and maintain the system operation status to ensure the basic operation of the entire power grid cloud microservice platform.

[0003] However, the current power grid cloud microservice platform technology has obvious defects. In terms of heterogeneous cluster resource management, the resource management of different computer rooms is scattered, like a pile of loose sand, unable to be uniformly scheduled, and resources cannot be coordinated and optimized, resulting in a large amount of idle resources, low resource utilization, and high operation and maintenance costs, making it difficult to cope with the dynamic changes in power grid business needs. In a heterogeneous resource environment, the microservice framework has chaotic technical specifications, and key mechanisms such as service discovery lack unified standards. It is as chaotic as the Tower of Babel, which makes developers deeply trapped in the quagmire of compatibility issues, greatly reduces development efficiency, and seriously affects the efficiency and stability of communication between services, greatly limiting the scalability of microservices. In addition, automated operation and maintenance and performance optimization are even weaker links. The intelligent monitoring system is in name only and cannot accurately detect potential problems in real time. The alarm threshold is set blindly, the fault prediction ability is missing, and the offline fault recovery strategy is imperfect. Once the system fails, it cannot be automatically repaired in time. The operation and maintenance personnel can only grope blindly, resulting in unstable system operation, seriously affecting the service quality, and it is difficult to ensure the reliable and stable operation of the power grid system. Summary of the invention

[0004] Based on this, it is necessary to provide a power grid heterogeneous cluster resource management method, cloud server, storage medium and computer program product to address the above technical problems.

[0005] In a first aspect, the present application provides a method for managing resources of a heterogeneous cluster of a power grid, which is applied to a cloud server, comprising:

[0006] Discover and integrate resources in heterogeneous power grid clusters to determine centralized integrated resources;

[0007] Obtaining operating parameters of software systems and status information of hardware devices in the power grid heterogeneous cluster;

[0008] Determining configuration parameters of the cloud service based on the operating parameters of the software system and the status information of the hardware device;

[0009] The centralized integrated resources are allocated based on service requirements and configuration parameters of the cloud service.

[0010] In one embodiment, the discovery and integration of resources in a heterogeneous power grid cluster to obtain centralized integrated resources includes:

[0011] Sending a probe request to each node in the heterogeneous cluster of the power grid;

[0012] and receiving response information for the detection request, wherein the response information includes feature identifiers and semantic information of resources in the node;

[0013] Determine the resource type and resource location based on the feature identifier and semantic information of the resource in the node;

[0014] According to the resource type corresponding to the resource in each node in the heterogeneous power grid cluster, convert the format and protocol of the resource in each node to obtain the resources in each node with a unified protocol and a unified format;

[0015] The resources in each node with unified protocol and unified format are associated and stored with corresponding resource types and resource locations to obtain the centralized integrated resources.

[0016] In one embodiment, determining the configuration parameters of the cloud service based on the operating parameters of the software system and the status information of the hardware device includes:

[0017] Determining environmental change information of the heterogeneous power grid cluster based on real-time detected operating parameters of the software system and status information of the hardware device;

[0018] Based on the environmental change information, a configuration parameter of the cloud service is determined.

[0019] In one embodiment, allocating resources to the centralized integrated resources based on service requirements and configuration parameters of the cloud service includes:

[0020] Analyze the resource types and quantities required in service requirements;

[0021] According to the required resource type and quantity, combined with the configuration parameters of the cloud service, and the resource idleness and resource location of the centralized integrated resources, matching resources are screened from the centralized integrated resources.

[0022] In one embodiment, the selecting matching resources from the centralized integrated resources according to the required resource type and quantity, in combination with the configuration parameters of the cloud service, and the resource idleness and resource location of the centralized integrated resources, includes:

[0023] Determining the priority of resources in the centralized integrated resources based on resource-related target factors, wherein the resource target factors include: at least one of performance indicators, availability, compatibility, and historical fault records;

[0024] Determining resource usage restriction conditions based on configuration parameters of the cloud service;

[0025] Based on the required resource type and quantity, the resource priority, the resource usage restriction condition, and the resource idleness and resource location of the centralized integrated resources, matching resources are screened from the centralized integrated resources.

[0026] In one embodiment, the method further comprises:

[0027] Continuously monitoring the resource status of resources in the heterogeneous cluster of the power grid and / or the operating parameters of the cloud service;

[0028] When it is determined that the resource state of the resource is abnormal and / or the operating parameters of the cloud service are abnormal, outputting warning information;

[0029] The warning information is output in at least one of the following ways:

[0030] SMS, email, system pop-up window, voice alarm.

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

[0032] Heterogeneous cluster resource aggregation module, used to discover and integrate resources in the heterogeneous cluster of the power grid to obtain centralized integrated resources;

[0033] A cloud service adaptation and optimization module, used to obtain the operating parameters of the software system and the status information of the hardware devices in the heterogeneous power grid cluster; based on the operating parameters of the software system and the status information of the hardware devices, determine the configuration parameters of the cloud service;

[0034] The resource collaborative scheduling module is used to allocate the centralized integrated resources based on service requirements and configuration parameters of the cloud service.

[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 power grid heterogeneous cluster resource management method can discover and integrate resources in the power grid heterogeneous cluster to determine the centralized integrated resources; obtain the operating parameters of the software system and the status information of the hardware equipment in the power grid heterogeneous cluster; determine the configuration parameters of the cloud service based on the operating parameters of the software system and the status information of the hardware equipment; and allocate the centralized integrated resources based on the service requirements and the configuration parameters of the cloud service. Through this solution, the effective integration of power grid heterogeneous cluster resources is realized, the scattered resources are centralized, the resource utilization rate is greatly improved, the operation and maintenance costs are reduced, and the dynamic changes of power grid business can be flexibly responded to. By accurately obtaining software and hardware related parameters to determine the cloud service configuration, resources are reasonably allocated according to service requirements, the efficient adaptation of services is guaranteed, the operation stability and reliability of the entire power grid cloud microservice platform are optimized, and a solid foundation is laid for the digital development of the power grid. 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 method for managing resources of a heterogeneous cluster of a power grid in an embodiment;

[0041] Figure 2 A schematic diagram of a flow chart of another method for managing resources of a heterogeneous cluster of a power grid in one embodiment;

[0042] Figure 3 A system structure block diagram of a cloud server in an embodiment;

[0043] Figure 4 It is a structural block diagram of a heterogeneous cluster resource aggregation module in one embodiment;

[0044] Figure 5 It is a structural block diagram of a cloud service adaptation and optimization module in one embodiment;

[0045] Figure 6 It is a structural block diagram of a resource collaborative scheduling module in one embodiment;

[0046] Figure 7 is a structural block diagram of an operating status monitoring module in an embodiment;

[0047] Figure 8 is a structural block diagram of a security management and protection module in one embodiment;

[0048] Fig. 9 1 is a diagram of the internal structure of a cloud server in one embodiment. DETAILED DESCRIPTION

[0049] 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.

[0050] With the continuous advancement of the digital transformation of power grids, the power grid cloud microservice platform has emerged. However, the platform has exposed a series of problems that need to be solved in the fields of heterogeneous cluster resource management and cloud services.

[0051] At the heterogeneous cluster resource management level, it is difficult to implement unified scheduling because the heterogeneous cluster resources in different computer rooms are in a decentralized management state. The resources of each cluster are isolated from each other and cannot achieve collaborative optimization, which directly leads to the dilemma of low resource utilization and high operation and maintenance costs. For example, some tasks are difficult to advance in clusters with tight resources, while at the same time, there are a large number of idle resources in other clusters. This uneven resource allocation makes it difficult to effectively improve the performance of the entire system, and it is also impossible to flexibly and efficiently respond to the dynamic changes in the power grid business. From the perspective of the existing microservice framework, when facing a heterogeneous resource environment, the problem of inconsistent technical specifications is particularly prominent, which leads to management chaos. Mechanisms such as service discovery, registration, configuration management, and load balancing lack unified consistency standards, just like people of different languages ​​communicating without rules, which greatly reduces development efficiency. Developers have to spend a lot of energy to deal with various compatibility issues, and it is difficult to focus all their attention on the implementation of business logic. In addition, the communication efficiency and stability between services are also greatly limited. It is impossible to effectively support different technology stacks and architectures, which seriously restricts the scalability of microservices. In terms of automated operation and maintenance and performance optimization, in a heterogeneous and complex microservice environment, there is a lack of effective intelligent monitoring systems, making it difficult to detect potential problems in real time. The setting of alarm thresholds lacks scientific rationality, the fault prediction capability is seriously insufficient, and the offline fault recovery strategy is also imperfect. Once a problem occurs in the system, it cannot be automatically repaired in time, and the operation and maintenance personnel can only explore and troubleshoot in the face of numerous difficulties, resulting in extremely unstable system performance and seriously affecting the service quality.

[0052] In summary, the current power grid microservice platform technology has many shortcomings in heterogeneous cluster resource management and cloud service assurance. There is an urgent need to achieve efficient management of heterogeneous clusters, unify technical specifications, and build a reliable automated operation and maintenance system to meet the development needs of power grid business and ensure that the power grid system can operate stably and reliably.

[0053] In order to solve the above problems, the embodiment of the present application provides a method for managing heterogeneous cluster resources of a power grid, which is applied to a cloud server. Through this solution, the effective integration of heterogeneous cluster resources of the power grid is realized, and the scattered resources are centralized, which greatly improves the resource utilization rate, reduces the operation and maintenance cost, and can flexibly respond to the dynamic changes of power grid business. By accurately obtaining software and hardware related parameters to determine the cloud service configuration, resources are reasonably allocated according to service requirements, and efficient adaptation of services is guaranteed, the operation stability and reliability of the entire power grid cloud microservice platform are optimized, and a solid foundation is laid for the digital development of the power grid.

[0054] In an exemplary embodiment, Figure 1 As shown, a flow chart of a method for managing heterogeneous cluster resources of a power grid is provided. The method can be executed by a cloud server. A heterogeneous cluster resource management platform of a power grid can be configured in the cloud server. The steps of the method can be specifically executed by the heterogeneous cluster resource management platform of the power grid. The method includes the following steps:

[0055] 101. Discover and integrate resources in heterogeneous power grid clusters to determine centralized integrated resources.

[0056] Among them, the power grid heterogeneous cluster refers to a collection of power grid-related computing resources composed of a variety of different architectures and technologies, and these resources are distributed on different nodes.

[0057] In some embodiments, the above-mentioned discovery and integration processing of resources in the power grid heterogeneous cluster to obtain centralized integrated resources includes: sending a detection request to each node in the power grid heterogeneous cluster; and receiving response information for the detection request, the response information including characteristic identification and semantic information of the resources in the node; determining the resource type and resource location based on the characteristic identification and semantic information of the resources in the node; converting the format and protocol of the resources in each node according to the resource type corresponding to the resources in each node in the power grid heterogeneous cluster to obtain resources in each node with a unified protocol and a unified format; and associating and storing the resources in each node with a unified protocol and a unified format with the corresponding resource type and resource location to obtain centralized integrated resources.

[0058] In some embodiments, when a probe request is sent to each node in a heterogeneous power grid cluster, the purpose of the "probe request" here is to search for potential available resources at each node and understand what resources are available on the node.

[0059] The response information received in response to the detection request includes the characteristic identification and semantic information of the resources in the node, wherein the "characteristic identification" is like the "identity card" of the resource, which can be used to quickly identify what type of resource it is, such as a certain type of server resource, a specific storage device resource, etc.; the "semantic information" can supplement the purpose of the resource, the business scope to which it belongs, etc., to help locate the resource more accurately. Based on the characteristic identification and semantic information of the resources in the node, it is possible to clarify the specific identity of the resource, such as computing resources, storage resources, or network resources, and at the same time know its physical or logical location in the entire cluster, which is convenient for subsequent management.

[0060] As mentioned above, different manufacturers and different architectures have different formats and protocols for resources. For example, some use old network protocols to transmit data, and some have special data storage formats. Here, we need to convert them all into the same common language form so that resources can communicate smoothly. After the format and protocol are unified, it is convenient for centralized management and control. The converted and smoothly communicated resources are saved together with their respective resource types, resource locations and other information to form a resource warehouse. This is to centrally integrate resources, and subsequent deployment can find resources from the resource warehouse.

[0061] For example, when performing format and protocol conversion, self-developed adaptation middleware is used. The middleware has built-in conversion modules for multiple common old network protocols, such as conversion from outdated Sequenced Packet Exchange (SPX) / Internetwork Packet Exchange (IPX) protocols to general Transmission Control Protocol (TCP) / Internet Protocol (IP) protocols. For data storage formats, with the help of open source format conversion tool libraries, such as the Data Format Converter Lib, the corresponding conversion functions are called according to the storage format characteristics of different resources to ensure the integrity and accuracy of the data during the conversion process. After actual testing, the data conversion success rate can reach more than 95%.

[0062] 102. Obtain the operating parameters of the software system and the status information of the hardware devices in the heterogeneous cluster of the power grid.

[0063] The operating parameters of the above software systems include: various software running in the heterogeneous power grid cluster, such as operating system version, middleware (bridge software connecting different software components) operating status, interdependencies between services, etc. These parameters can reflect whether the software runs smoothly, whether there are potential conflicts, and risks of freezing. For example, if the CPU usage of the operating system is too high, it may indicate low software operation efficiency.

[0064] The status information of the above hardware devices refers to the hardware conditions, such as the CPU usage of the server, which reflects how much of the current hardware computing power is occupied; memory usage, which shows how much memory space is available for new tasks; storage capacity, which lets us know how much space is left on the hard disk to store data. Combining these hardware states can determine whether the entire cluster hardware can meet the requirements of subsequent tasks.

[0065] For example, the frequency of obtaining the operating parameters of the software system and the status information of the hardware equipment in the heterogeneous power grid cluster can also be set. For example, the frequency can be set to once every 5 minutes. This will not lead to excessive consumption of system resources due to too frequent collection, but also ensure that the dynamic changes of the system are captured in time. For some key business software or hardware equipment prone to failure, the collection frequency can be increased to once every 1 minute as needed to ensure close monitoring of its operating status.

[0066] 103. Determine the configuration parameters of the cloud service based on the operating parameters of the software system and the status information of the hardware device.

[0067] Among them, cloud services refer to a series of applications, functions or resources provided to users on demand and in an easily scalable manner through the Internet. In the context of heterogeneous cluster resource management in power grids, cloud services are a set of services built specifically for power grid business operations in the cloud. It covers various functional modules that support power grid data processing, business logic operation, resource allocation decisions, etc., and can meet the complex and diverse business needs of the power grid. Specific business scenarios such as power data analysis, real-time power grid monitoring data processing, and fault warning information push all rely on cloud services for efficient and stable execution. Cloud servers are the hardware infrastructure that carries these cloud services. They are physical devices at the physical level, usually composed of a large number of high-performance processors, memory, storage devices, and network components, distributed in data centers, and have powerful computing and storage capabilities. Cloud services rely on cloud servers to run. Cloud servers provide the necessary hardware resource guarantees for cloud services, including computing resources (such as the number of CPU cores and running time), storage resources (such as data storage space) and network resources (such as bandwidth and network latency guarantees), etc., so that cloud services can be instantiated on them and provide services to the outside world. Cloud services, on the basis of cloud servers, realize specific business value for users through software-level architecture design, algorithm optimization, and functional integration.

[0068] In some embodiments, the above-mentioned determination of the configuration parameters of the cloud service based on the operating parameters of the software system and the status information of the hardware equipment may include: determining the environmental change information of the heterogeneous cluster of the power grid based on the operating parameters of the software system and the status information of the hardware equipment detected in real time; determining the configuration parameters of the cloud service based on the environmental change information.

[0069] In some embodiments, in the process of determining the environmental change information of the heterogeneous cluster of the power grid based on the operating parameters of the software system and the status information of the hardware equipment detected in real time, the parameters of the software and hardware can be continuously observed. When it is found that a certain software module frequently reports errors or a certain disk of the hardware reads and writes slowly, these signs indicate that the cluster environment has changed, which may be caused by resource shortages, hardware aging, software conflicts, etc.

[0070] For example, in addition to software errors and slow hardware disk read and write, if the network delay is monitored to increase suddenly, exceeding 50% of the normal threshold, or the memory leak of the software system continues to grow, and the leakage volume exceeds 10MB per minute, it is also determined that the power grid heterogeneous cluster environment has changed. At the same time, in order to accurately locate the cause of environmental changes, an intelligent root cause analysis algorithm can be used. The algorithm can combine historical data and real-time monitoring data to build a fault tree model, which can quickly trace the root cause of the problem, such as determining whether a certain hardware driver is incompatible with the system after the update, causing a software conflict, or whether the hardware performance has deteriorated due to long-term high-load operation.

[0071] In some embodiments, in the process of determining the configuration parameters of cloud services based on environmental change information, once the environment changes, the cloud services (the set of services that provide support for power grid business operations in the cloud) must be adjusted accordingly. For example, if the hardware CPU usage rate is found to be over 80% for a long time, the cloud service may reduce the application of CPU resources for some non-critical tasks and reallocate resources, such as adjusting the memory size and network bandwidth allocated to a certain service to ensure the stability of the overall service. The cloud service configuration parameters are adjusted here.

[0072] 104. Allocate resources to centralized integrated resources based on service requirements and configuration parameters of cloud services.

[0073] In some embodiments, allocating resources to centralized integrated resources based on service requirements and configuration parameters of cloud services may include: parsing the required resource types and quantities in the service requirements; screening matching resources from the centralized integrated resources based on the required resource types and quantities, combined with the configuration parameters of the cloud services, and the resource idleness and resource location of the centralized integrated resources.

[0074] Among them, when analyzing the resource types and quantities required in the service requirements, when there is a power grid business to be carried out, the demand for resources will be put forward. For example, the power data analysis business may require a large amount of computing resources (such as 10 CPU cores, running for 2 hours), certain storage resources (500GB data storage space), etc. Here, the business requirements are broken down into specific resource types and quantities to facilitate subsequent resource matching.

[0075] In some embodiments, matching resources are screened from the centralized integrated resources based on the required resource type and quantity, in combination with the configuration parameters of the cloud service, and the resource freeness and resource location of the centralized integrated resources, including: determining the priority of resources in the centralized integrated resources based on resource-related target factors; determining resource usage restrictions based on the configuration parameters of the cloud service; and screening matching resources from the centralized integrated resources based on the required resource type and quantity, resource priority, resource usage restrictions, and the resource freeness and resource location of the centralized integrated resources.

[0076] The target factors of the resources include at least one of performance indicators, availability, compatibility, and historical fault records.

[0077] With the required resource types and quantities, check how much resource support the cloud service can provide under the current settings. At the same time, consider which resources in the centralized integrated resources in the resource library are idle (i.e., the degree of resource idleness) and the distance from the business initiation site (i.e., resource location). Based on these factors, select the most suitable resources from the resource library and allocate them to the business.

[0078] Exemplarily, based on resource-related target factors, the priority of resources in the centralized integrated resources may include but is not limited to: "Performance indicators" in "target factors" such as resource processing speed, response time, etc. It can be considered that high-performance resources with high resource processing speed and short response time have higher priority; "availability" refers to whether the resource is stable and reliable. It can be considered that resources that always fail have lower availability and correspondingly lower priority; "compatibility" is related to whether it can be shared with business software and other resources; "Historical fault records" If there are frequent problems, the priority will also be set lower. The above considerations can be combined to rank the priority of resources.

[0079] For example, in different business scenarios, the importance of various factors is different, and their weights can be adjusted accordingly. For example, in general power grid business scenarios, the weight of performance indicators is set to 40%, the weight of availability is 30%, the weight of compatibility is 20%, and the weight of historical fault records is 10%. However, for businesses such as real-time repair of power failures that require extremely high response timeliness, the weight of performance indicators can be increased to 60% to ensure that resources with fast processing speed and short response time are allocated first; and for some newly launched businesses that are in the debugging stage, the compatibility weight can be increased to 40%, giving priority to ensuring integration with existing systems and resources to avoid compatibility issues that affect business advancement. Through this dynamic weight allocation mechanism, resource screening and matching can be more in line with actual business needs.

[0080] In some embodiments, the power grid heterogeneous cluster resource management method provided in the embodiments of the present application can also continuously monitor the resource status of the resources in the power grid heterogeneous cluster and / or the operating parameters of the cloud service; and output warning information when it is determined that the resource status of the resources is abnormal and / or the operating parameters of the cloud service are abnormal.

[0081] The warning information is output in at least one of the following ways:

[0082] SMS, email, system pop-up window, voice alarm.

[0083] like Figure 2 As shown, a flow chart of a method for managing heterogeneous cluster resources of a power grid is also provided. The method can be executed by a cloud server. A heterogeneous cluster resource management platform of a power grid can be configured in the cloud server. The steps of the method can be specifically executed by the heterogeneous cluster resource management platform of the power grid. The method includes the following steps:

[0084] 201. Discover and integrate resources in heterogeneous power grid clusters to determine centralized integrated resources.

[0085] 202. Obtain operating parameters of software systems and status information of hardware devices in a heterogeneous power grid cluster.

[0086] 203. Determine configuration parameters of the cloud service based on the operating parameters of the software system and the status information of the hardware device.

[0087] 204. Allocate resources for centralized integration based on service requirements and configuration parameters of cloud services.

[0088] The description of the above steps 201 to 204 can refer to the relevant description of the above steps 101 to 104, which will not be repeated here.

[0089] 205. Continuously monitor the resource status of resources in the heterogeneous cluster of the power grid and / or the operating parameters of the cloud service.

[0090] In some embodiments, a dedicated monitoring program or module can be run on a cloud server to monitor all key links in the heterogeneous power grid cluster at all times. A hybrid mode of timed polling combined with event-driven can be adopted. On the one hand, various types of information are actively collected at set time intervals, such as every 30 seconds; on the other hand, when a specific event occurs in the system, such as new resource access, service restart, etc., the monitoring program can be immediately triggered to perform targeted data capture.

[0091] The resource status can include: resource busyness, such as CPU utilization and GPU load of computing resources, read and write busyness and remaining available space of storage resources, and bandwidth usage and network latency of network resources. Taking CPU utilization as an example, if it exceeds 70% for a long time, it may indicate that the computing node is facing high load pressure, and subsequent task allocation needs to be carefully considered; if the remaining available space of storage resources is less than 10%, there is a risk of storage overflow, and it is necessary to plan capacity expansion or clean up data in advance.

[0092] The operating parameters of the above cloud services may include the response time, throughput and error rate of cloud services. The response time of cloud services refers to the time from when the client sends a request to when the server receives feedback. For power grid control services with high real-time requirements, the response time must be controlled in milliseconds. The throughput of cloud services can reflect the number of requests that the service can handle per unit time, which is related to the concurrent processing capability of the service. If the throughput continues to decline, it may mean that the service has a bottleneck. The error rate of cloud services, such as the proportion of service request failures caused by code anomalies and insufficient resources, once the error rate increases, it is necessary to promptly investigate the root cause of the fault.

[0093] 206. When it is determined that the resource status of the resource is abnormal and / or the operating parameters of the cloud service are abnormal, output warning information.

[0094] In some embodiments, in the process of determining whether the resource status is abnormal, it can be judged by comparing with the preset normal threshold range. For example, a reasonable interval is set for each resource indicator. For example, the normal network delay should be between 10 and 50 milliseconds. When the delay is monitored to suddenly soar to 200 milliseconds, exceeding the upper limit, the network resource status is determined to be abnormal; for memory resources, if the free memory continues to be less than 5% of the total memory for more than 5 minutes, it is considered that the memory resource is tight and abnormal. The setting of the threshold here is not fixed, and can be dynamically adjusted based on the statistical analysis of historical data to adapt to the fluctuation characteristics of power grid business in different periods.

[0095] In some embodiments, taking the response time of cloud services as an example, when judging that the operating parameters of cloud services are abnormal, if the average response time of a key service increases suddenly from the normal 50 milliseconds to 500 milliseconds, and exceeds the preset 150 millisecond alarm threshold for more than 10 consecutive times, the response of the cloud service is deemed to be abnormal.

[0096] In some embodiments, when an abnormality is detected, the system can immediately start the early warning process. The early warning information will reach the relevant operation and maintenance personnel in a variety of ways according to the pre-configured notification strategy. "SMS" notification can ensure that the operation and maintenance personnel can receive the alarm on their mobile phones as soon as possible even if they are not in front of the computer, such as "The CPU utilization rate of node A in the heterogeneous power grid cluster exceeds 90%, please deal with it in time"; "Email" records the time, location, and abnormal indicator details of the abnormality in detail, which is convenient for subsequent retrospective analysis and is suitable for non-emergency but detailed records. The "system pop-up window" directly pops up a striking alarm box on the operation and maintenance monitoring system interface to display key abnormal information, which is convenient for the on-duty personnel to respond quickly; "Voice alarm" broadcasts the abnormal content in a clear and loud voice, which can quickly attract attention in a noisy operation and maintenance environment, especially for urgent and serious abnormalities that need to be handled immediately, such as core service crashes, etc., to ensure that the operation and maintenance team can know and solve the problem in time, and ensure the stable operation of the power grid heterogeneous cluster resource management platform and cloud services.

[0097] The power grid heterogeneous cluster resource management method shown in Figure 2 can timely and comprehensively grasp the operation status of the system by continuously monitoring the resource status of resources in the power grid heterogeneous cluster and the operating parameters of cloud services. The hybrid mode of timed polling combined with event-driven can ensure the timeliness and accuracy of information collection. The careful monitoring of resource status and operating parameters helps to discover potential problems in advance, such as resource shortages and service bottlenecks. When determining anomalies, the anomalies can be accurately located by comparing with the dynamically adjusted preset thresholds. The output of early warning information in multiple ways enables operation and maintenance personnel to respond quickly, ensure the stable operation of the power grid heterogeneous cluster resource management platform and cloud services, and improve the reliability and efficiency of power grid services.

[0098] The power grid heterogeneous cluster resource management method provided in the embodiment of the present application can also ensure the data security, network security and access security of heterogeneous cluster resources and cloud services through various security technical means.

[0099] 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.

[0100] Based on the same inventive concept, the embodiment of the present application also provides a cloud server for implementing the above-mentioned method for managing resources of a heterogeneous cluster of power grids. The implementation scheme for solving the problem provided by the cloud server is similar to the implementation scheme 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 method for managing resources of a heterogeneous cluster of power grids above, and will not be repeated here.

[0101] In an exemplary embodiment, Figure 3 As shown, a cloud server is provided, including:

[0102] The heterogeneous cluster resource aggregation module 301 is used to discover and integrate the resources in the heterogeneous cluster of the power grid to obtain centralized integrated resources;

[0103] The cloud service adaptation optimization module 302 is used to obtain the operating parameters of the software system and the status information of the hardware devices in the heterogeneous power grid cluster; based on the operating parameters of the software system and the status information of the hardware devices, determine the configuration parameters of the cloud service;

[0104] The resource collaborative scheduling module 303 is used to allocate centralized integrated resources based on service requirements and configuration parameters of cloud services.

[0105] In some embodiments, the heterogeneous cluster resource aggregation module 301 is specifically used to:

[0106] Sending a probe request to each node in the heterogeneous cluster of the power grid;

[0107] and receiving response information for the detection request, the response information including feature identifiers and semantic information of resources in the node;

[0108] Determine resource type and resource location based on feature identification and semantic information of resources in the node;

[0109] According to the resource types corresponding to the resources in each node in the heterogeneous power grid cluster, the formats and protocols of the resources in each node are converted to obtain the resources in each node with unified protocols and formats;

[0110] The resources in each node with unified protocol and unified format are associated and stored with the corresponding resource type and resource location to obtain centralized integrated resources.

[0111] In some embodiments, the cloud service adaptation optimization module 302 is specifically used to:

[0112] Determine the environmental change information of the heterogeneous power grid cluster based on the operating parameters of the software system and the status information of the hardware equipment detected in real time;

[0113] Determine the configuration parameters of cloud services based on environmental change information.

[0114] In some embodiments, the resource collaborative scheduling module 303 is specifically used to:

[0115] Analyze the resource types and quantities required in service requirements;

[0116] According to the required resource type and quantity, combined with the configuration parameters of the cloud service, and the resource availability and resource location of the centralized integrated resources, matching resources are selected from the centralized integrated resources.

[0117] In some embodiments, the resource collaborative scheduling module 303 is specifically used to:

[0118] Determine the priority of resources in the centralized integration resources based on resource-related target factors, where the resource target factors include: at least one of performance indicators, availability, compatibility, and historical fault records;

[0119] Determine resource usage constraints based on the configuration parameters of the cloud service;

[0120] Based on the required resource type and quantity, resource priority, resource usage constraints, and resource availability and resource location of the centralized integrated resources, matching resources are selected from the centralized integrated resources.

[0121] In some embodiments, the cloud server further includes: an operation status monitoring module 304 for continuously monitoring the resource status of resources in the heterogeneous cluster of the power grid, and / or the operation parameters of the cloud service;

[0122] When it is determined that the resource status of the resource is abnormal and / or the operating parameters of the cloud service are abnormal, outputting warning information;

[0123] Warning information is output in at least one of the following ways:

[0124] SMS, email, system pop-up window, voice alarm.

[0125] In some embodiments, the cloud server may further include: a security management and protection module 305 for ensuring data security, network security and access security of heterogeneous cluster resources and cloud services through a variety of security technical means.

[0126] Exemplarily, as shown in FIG. 4 , it is a structural block diagram of a heterogeneous cluster resource aggregation module 301. The heterogeneous cluster resource aggregation module 301 in FIG. 4 includes:

[0127] The resource search unit 3011 is used to search for accessible heterogeneous cluster resources in the power grid network using a variety of detection technologies, including network protocol scanning, port detection and feature recognition technology. By sending a detection request to each node in the power grid network, it is determined whether it is a heterogeneous cluster resource based on the feature identifier in the node response information, and the resource type and location are determined;

[0128] The interface adaptation unit 3012 is used to develop conversion components that adapt to different types of resource interfaces, so as to achieve smooth access to resources. The conversion component has built-in conversion components for multiple resource input interfaces, so as to achieve smooth access to resources. The conversion component has built-in multiple interface protocol conversion logics, which can automatically match the corresponding conversion rules according to the interface type of the access resource, convert interfaces of different formats and protocols into a unified standard interface, and ensure that the accuracy of data transmission is not less than 98% during the interface conversion process;

[0129] The resource registration unit 3013 is used to classify and register the accessed resources according to their type, performance, cluster and other attributes, record the resource identification information, configuration parameters, access time and other key data in detail, and store them in the database for subsequent management and scheduling.

[0130] Among them, the above-mentioned interface adaptation unit 12 has the ability to dynamically load different interface drivers to adapt to various types of resource interfaces, and ensure that the accuracy of data transmission is not less than 98% during the interface conversion process. Its dynamic loading mechanism monitors the interface type of the access resource in real time and automatically loads the corresponding driver from the driver library. The driver library supports online updating and expansion to adapt to new interface types.

[0131] As shown in FIG. 5 , it is a structural block diagram of a cloud service adaptation and optimization module 302. The cloud service adaptation and optimization module 302 in FIG. 5 includes:

[0132] The environment sensing unit 3021 is used to sense the hardware and software environment changes of the heterogeneous cluster in real time, and regularly collects the status information of the hardware devices, such as CPU usage, memory usage, storage capacity, etc., and the operating parameters of the software system, such as operating system version, middleware status, service dependency, etc., and uses data collection probes and sensor technology to achieve real-time monitoring of environmental changes;

[0133] The configuration adjustment unit 3022 is used to automatically adjust the configuration parameters of the cloud service according to environmental changes. With the information obtained by the environment perception unit, the preset configuration adjustment strategy and algorithm are used to dynamically modify the resource allocation strategy, network configuration, operation parameters, etc. of the service to ensure that the service can achieve the best performance in different environments. The adjustment process supports online real-time adjustment without interrupting the service operation.

[0134] The performance optimization unit 3023 is used to improve the working performance of cloud services in heterogeneous environments by using optimization algorithms. The optimization algorithm includes a resource prediction model based on machine learning. It predicts the future resource requirements of services based on historical resource usage data and real-time monitoring data, and pre-allocates resources in advance. At the same time, it combines the load balancing algorithm to dynamically adjust the distribution of service instances in heterogeneous clusters, and give priority to resource allocation and parameter adjustment of key business processes.

[0135] Among them, the performance optimization unit 3023, when optimizing the operating performance of cloud services, gives priority to resource allocation and parameter adjustment of key business processes. By prioritizing business processes and monitoring performance indicators, more resources are allocated to key business processes. At the same time, a dynamic adjustment algorithm is used to optimize the parameter settings of key processes in real time according to changes in business load.

[0136] As shown in FIG. 6 , it is a structural block diagram of a resource collaborative scheduling module 303. The resource collaborative scheduling module 303 in FIG. 6 includes:

[0137] The demand analysis unit 3031 is used to analyze the type and quantity of resource requirements of the cloud service, and accurately determine the required computing resources, such as the number of CPU cores, computing time, storage resources, such as data storage capacity, read and write speed requirements, network resources, such as bandwidth, network delay requirements, etc. by analyzing the task request and business logic of the service;

[0138] The resource matching unit 3032 is used to accurately match the resources that meet the requirements in the heterogeneous cluster resources, and select the resources that meet the conditions of performance, availability, compatibility, etc. in the resource library according to the results of the demand analysis unit, and use the resource matching algorithm to comprehensively consider the idleness of resources, load balancing, geographical location and other factors to select the optimal resource combination;

[0139] The scheduling execution unit 3033 is used to allocate the matched resources to the services according to the predetermined strategy, monitor the resource usage, adopt a distributed architecture to perform resource allocation operations, improve the parallel processing capability of resource allocation, use message queues and distributed transaction management technology to ensure the accuracy and consistency of resource allocation, monitor the resource usage status in real time during the resource allocation process, such as resource utilization rate, service response time, etc., and dynamically adjust the resource allocation strategy according to the monitoring results.

[0140] Among them, the scheduling execution unit 3033 adopts a distributed architecture to perform resource allocation operations to improve the parallel processing capabilities of resource allocation. The distributed architecture is built based on a distributed computing framework, and uses distributed locks and distributed transactions to ensure the atomicity and consistency of resource allocation. At the same time, the resource allocation tasks are evenly distributed to multiple nodes for execution through a load balancing strategy.

[0141] As shown in FIG. 7 , it is a structural block diagram of an operation status monitoring module 304. The operation status monitoring module 304 in FIG. 7 includes:

[0142] The data collection unit 3041 is used to regularly collect the operation data of heterogeneous cluster resources and cloud services. It adopts a distributed data collection architecture and deploys collection agents on cluster nodes and service instances. It collects data such as system logs, performance indicators, network traffic, etc. according to the predetermined collection cycle and collection indicators. The collection process supports multiple data formats and protocols to ensure the integrity and accuracy of the data.

[0143] The data analysis unit 3042 is used to use the data analysis algorithm to determine whether the operation status is normal, and to use the machine learning algorithm and the threshold judgment method to analyze the collected data in real time, build a normal operation status model, and identify abnormal situations such as excessive resource utilization and long service response time by comparing the deviation between the actual data and the model, and at the same time locate the location and cause of the abnormality;

[0144] The warning notification unit 3043 is used to issue a warning notification in time when an abnormality is found. It has the function of customizing the warning threshold and the notification object grouping, so as to provide targeted notifications according to different abnormality types and severities. It supports multiple notification methods, such as SMS, email, system pop-up windows, voice alarms, etc., to ensure that relevant operation and maintenance personnel can receive notifications in time and take corresponding measures.

[0145] Among them, the early warning notification unit 3043 has the function of customizing early warning thresholds and notification object grouping, so as to provide targeted notifications based on different abnormality types and severities. The early warning thresholds and notification object grouping rules can be flexibly set through the configuration file and user interface. At the same time, it supports dynamic adjustment of the early warning level, and automatically increases the early warning level based on the duration of the abnormality and the scope of impact.

[0146] Exemplarily, as shown in FIG8 , a security management and protection module 305 is shown. As shown in FIG8 , a structural block diagram of a security management and protection module 305 is shown. The security management and protection module 305 in FIG8 includes:

[0147] The data encryption unit 3051 is used to encrypt sensitive data. It uses a variety of encryption algorithms, such as AES, RSA, etc., and selects a suitable encryption method according to the importance and sensitivity of the data. It encrypts the data during data storage and transmission to ensure the confidentiality of the data, and supports dynamic management and secure storage of encryption keys.

[0148] The identity authentication unit 3052 is used to verify the identity of users who access resources and services. It supports password strength detection and regular mandatory password update mechanisms during password authentication. It also integrates multiple authentication methods, such as fingerprint authentication, digital certificate authentication, dynamic password authentication, etc. It selects the appropriate authentication method based on the user's security level and business requirements to ensure the authenticity of the user's identity.

[0149] The access control unit 3053 is used to restrict access to resources and services according to user permissions. The role-based access control RBAC model defines corresponding access permissions for different user roles, and authorizes and verifies user access requests through the permission management system to ensure that users can only access resources and services for which they have permissions. At the same time, the user's access log is recorded for subsequent auditing and tracing.

[0150] Among them, the identity authentication unit 3052 supports password strength detection and regular mandatory password update mechanism during password authentication. The password strength detection adopts password complexity rules and dictionary attack detection algorithm. The regular mandatory password update mechanism reminds users to update their passwords according to a preset time period. Users must follow the password strength rules when updating their passwords.

[0151] 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.

[0152] In an exemplary embodiment, a cloud server is provided, and the internal structure diagram of the cloud server can be as follows: Fig. 9 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 resources of a heterogeneous cluster of power grids is implemented.

[0153] Those skilled in the art will understand that Fig. 9 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.

[0154] 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 method for managing resources of a heterogeneous cluster of power grids provided in the above method embodiment is implemented.

[0155] 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 method for managing resources of a heterogeneous cluster of power grids provided in the above method embodiment is implemented.

[0156] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the method for managing resources of a heterogeneous cluster in a power grid provided in the above method embodiment.

[0157] 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.

[0158] 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.

[0159] 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 managing resources of a heterogeneous power grid cluster, characterized in that: Applied to cloud servers, including: Discover and integrate resources in heterogeneous power grid clusters to determine centralized integrated resources; Obtaining operating parameters of software systems and status information of hardware devices in the power grid heterogeneous cluster; Determining configuration parameters of the cloud service based on the operating parameters of the software system and the status information of the hardware device; The centralized integrated resources are allocated based on service requirements and configuration parameters of the cloud service.

2. The method according to claim 1, characterized in that The discovery and integration of resources in the heterogeneous power grid cluster to obtain centralized integrated resources includes: Sending a probe request to each node in the heterogeneous cluster of the power grid; and receiving response information for the detection request, wherein the response information includes feature identifiers and semantic information of resources in the node; Determine the resource type and resource location based on the feature identifier and semantic information of the resource in the node; According to the resource type corresponding to the resource in each node in the heterogeneous power grid cluster, convert the format and protocol of the resource in each node to obtain the resources in each node with a unified protocol and a unified format; The resources in each node with unified protocol and unified format are associated and stored with corresponding resource types and resource locations to obtain the centralized integrated resources.

3. The method according to claim 1, characterized in that Determining configuration parameters of the cloud service based on the operating parameters of the software system and the status information of the hardware device includes: Determining environmental change information of the heterogeneous power grid cluster based on real-time detected operating parameters of the software system and status information of the hardware device; Based on the environmental change information, a configuration parameter of the cloud service is determined.

4. The method according to claim 1, characterized in that Allocating the centralized integrated resources based on service requirements and configuration parameters of the cloud service includes: Analyze the resource types and quantities required in service requirements; According to the required resource type and quantity, combined with the configuration parameters of the cloud service, and the resource idleness and resource location of the centralized integrated resources, matching resources are screened from the centralized integrated resources.

5. The method according to claim 1, characterized in that The step of selecting matching resources from the centralized integrated resources based on the required resource type and quantity, in combination with the configuration parameters of the cloud service, and the resource idleness and resource location of the centralized integrated resources, includes: Determining the priority of resources in the centralized integrated resources based on resource-related target factors, wherein the resource target factors include: at least one of performance indicators, availability, compatibility, and historical fault records; Determining resource usage restriction conditions based on configuration parameters of the cloud service; Based on the required resource type and quantity, the resource priority, the resource usage restriction condition, and the resource idleness and resource location of the centralized integrated resources, matching resources are screened from the centralized integrated resources.

6. The method according to claim 1, characterized in that The method further comprises: Continuously monitoring the resource status of resources in the heterogeneous cluster of the power grid and / or the operating parameters of the cloud service; When it is determined that the resource state of the resource is abnormal and / or the operating parameters of the cloud service are abnormal, outputting warning information; The warning information is output in at least one of the following ways: SMS, email, system pop-up window, voice alarm.

7. A cloud server, characterized in that: include: Heterogeneous cluster resource aggregation module, used to discover and integrate resources in the heterogeneous cluster of the power grid to obtain centralized integrated resources; A cloud service adaptation and optimization module, used to obtain the operating parameters of the software system and the status information of the hardware devices in the heterogeneous power grid cluster; Determining configuration parameters of the cloud service based on the operating parameters of the software system and the status information of the hardware device; The resource collaborative scheduling module is used to allocate the centralized integrated resources based on service requirements and configuration parameters of the cloud service.

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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