A heterogeneous multi-core coexistence management system and method for cloud computing

Abstract

The present invention provides a heterogeneous multi-core coexistence management system and method for cloud computing, which relates to the technical field of cloud platforms. The system includes: a cluster information acquisition module, which is used to acquire the device information and performance information of server clusters with multiple different cores in the system; a container establishment module, which is used to establish containers by using a preset container technology and construct a mapping network between the containers and the server clusters; an interconnection establishment module, which is used to establish a unified API interface and middleware and output cluster interconnection information; a multi-core resource scheduling module, which is used to obtain a resource scheduling strategy and a resource scheduling method that match the real-time demand-resource view, and allocate the tasks in the application to the nodes under the corresponding server clusters in combination with the cluster interconnection information. By utilizing the advantages of different cloud platforms, the present invention realizes the flexible and dynamic configuration of heterogeneous multi-core cloud platform resources, improves the utilization rate of resources, and reduces the risks caused by binding to a single cloud platform.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud platforms, and particularly to a heterogeneous multi-core coexistence management system and method for cloud computing. Background Art

[0002] In a cloud computing environment, tasks are usually computed and stored on large-scale clusters to improve performance and efficiency. However, with the rapid growth of data volume, the demand for computing power in new types of services, and the steady development of domestic independent and controllable hardware, the computing power of traditional single-type chips can no longer meet the requirements, and the development of diversified computing power has become a trend.

[0003] Currently, due to differences in the performance and characteristics of computing power resources among different platform architectures, and at the same time, there may be differences in the data transmission speed between heterogeneous computing power nodes, there are problems such as low data transmission efficiency between platforms, large transmission delays, scattered platform resources, and low resource utilization.

[0004] Therefore, the present invention provides a heterogeneous multi-core coexistence management system and method for cloud computing. Summary of the Invention

[0005] The present invention provides a heterogeneous multi-core coexistence management system and method for cloud computing, which is used to utilize the advantages of different cloud platforms, realize the flexible and dynamic configuration of heterogeneous multi-core cloud platform resources, improve the resource utilization rate, enhance the flexibility and scalability of the heterogeneous multi-core cloud platform, and reduce the risks caused by binding to a single cloud platform.

[0006] On the one hand, the present invention provides a heterogeneous multi-core coexistence management system for cloud computing, including:

[0007] A cluster information acquisition module, configured to acquire the device information and performance information of multiple different-core server clusters in the system, and summarize and output multi-core heterogeneous cluster information;

[0008] A container establishment module, configured to establish multiple containers based on the multi-core heterogeneous cluster information by using a preset container technology, and construct a mapping network between each container and each server cluster;

[0009] An interconnection establishment module, configured to establish a unified API interface and middleware based on the multi-core heterogeneous cluster information and the mapping network, and output cluster interconnection information;

[0010] The multi-core resource scheduling module is used to obtain the application requirement information of the system and the resource information of each server cluster, output a real-time demand-resource view, and obtain a resource scheduling policy and a resource scheduling method that match the real-time demand-resource view in the policy-method database. Based on the resource scheduling policy and the resource scheduling method, and combined with the cluster interconnection information, tasks in the application are assigned to nodes under the corresponding server cluster.

[0011] Preferably, the cluster information acquisition module includes:

[0012] The cluster category information acquisition sub-module is used to obtain the type information of each server cluster in the system and output the cluster category information;

[0013] The chip-device-performance information acquisition sub-module is used to obtain the chip environment information corresponding to each type of server cluster in the cluster category information, as well as the device information and performance information under the corresponding chip environment, and summarize and output the multi-core heterogeneous cluster information.

[0014] Preferably, the container establishment module includes:

[0015] The feature extraction sub-module is used to extract features from the multi-core heterogeneous cluster information and construct a first feature set based on the extracted features;

[0016] The container construction sub-module is used to select at least two preset container technologies that match the first feature set in the method database in combination with a preset feature-technology mapping table, and establish multiple containers based on the preset container technologies;

[0017] The network construction sub-module is used to establish a mapping network between each container and each server cluster.

[0018] Preferably, the interconnection establishment module includes:

[0019] The interface information acquisition sub-module is used to obtain the interface information of each server cluster in the multi-core heterogeneous cluster information and output the cluster interface information;

[0020] The interface unification sub-module is used to configure the API gateway of each server cluster based on the cluster interface information and establish a unified API interface;

[0021] The middleware establishment sub-module is used to select a middleware that matches the middleware function requirement information in the method database based on the obtained middleware function requirement information;

[0022] The interconnection information establishment sub-module is used to output the cluster interconnection information based on the unified API interface and the middleware.

[0023] Preferably, the middleware establishment sub-module includes:

[0024] A requirement acquisition unit for acquiring the functional requirement information for mutual communication and data exchange between server clusters.

[0025] A middleware matching unit for selecting a matching middleware from the method database based on the functional requirement information.

[0026] A middleware configuration and optimization unit for establishing the architecture of the middleware, developing and integrating the middleware based on the architecture, and at the same time, performing performance testing on the middleware, and optimizing the performance and expanding the functions of the middleware in combination with the performance test results and the functional requirement information.

[0027] Preferably, the multi-core resource scheduling module includes:

[0028] An application requirement acquisition sub-module for outputting first requirement information based on the requirement information of each application in the system obtained in real time.

[0029] A management requirement acquisition sub-module for acquiring the unified management requirement information of the system and outputting second requirement information.

[0030] A requirement information generation sub-module for summarizing the first requirement information and the second requirement information and outputting cluster requirement information.

[0031] A resource data acquisition sub-module for acquiring the resource data and the corresponding resource status of each server cluster in the system in real time and outputting cluster resource information.

[0032] A view construction sub-module for constructing a real-time requirement-resource view based on the cluster requirement information and the cluster resource information and using a preset view construction method.

[0033] A requirement-resource matching sub-module for matching the requirement information and the resource information in the real-time requirement-resource view and outputting a requirement-resource matching result.

[0034] A factor acquisition sub-module for obtaining a first screening factor matching the requirement-resource matching result by combining a preset result-factor comparison table.

[0035] A policy-method matching sub-module for selecting a resource scheduling policy that meets a preset screening condition and a resource scheduling method corresponding to the resource scheduling policy from a policy-method database based on the first screening factor.

[0036] A task allocation sub-module, which is used to allocate various tasks in the application to the node resources under the corresponding server cluster based on the resource scheduling policy and resource scheduling method, and in combination with the cluster interconnection information, and output the resource scheduling process data;

[0037] An index acquisition sub-module, which is used to select corresponding performance evaluation indexes from the index database based on the acquired index selection instruction;

[0038] An effect analysis sub-module, which is used to perform effect analysis on the resource scheduling process data based on the performance evaluation indexes and output a first result;

[0039] An optimization analysis sub-module, which is used to perform optimization analysis on the first result by combining the optimization indexes selected from the index database and output an optimization analysis result;

[0040] A policy-method optimization sub-module, which is used to select a preset optimization method matching the optimization analysis result from the method database and optimize the resource scheduling policy and resource scheduling method based on the preset optimization method.

[0041] Preferably, the effect analysis sub-module includes:

[0042] An index division unit, which is used to divide the performance evaluation indexes to obtain a main index set and a secondary index set;

[0043] A weight acquisition unit, which is used to acquire the first weight of each performance evaluation index in the main index set and the second weight of each performance evaluation index in the secondary index set, and output an index-weight comparison table;

[0044] An index relationship acquisition unit, which is used to acquire the first relationship between the performance evaluation indexes in the main index set, the second relationship between the performance evaluation indexes in the secondary index set, and the third relationship between the performance evaluation indexes in the main index set and the secondary index set, and construct an index relationship graph;

[0045] A data parsing unit, which is used to parse the resource scheduling process data to obtain data to be evaluated;

[0046] An effect analysis unit, which is used to perform effect analysis on the data to be evaluated based on the main index set, the secondary index set, the index-weight comparison table and the index relationship graph to obtain an effect analysis result;

[0047] ;

[0048] Among them, represents the comprehensive effect analysis value based on the main index set and the secondary index set; represents an exponential function; Indicates the cross-correlation coefficient between the main index set and the secondary index set; Indicates the calculation weight of the effect analysis result corresponding to the main index set; Indicates the total number of performance evaluation indicators in the main index set; Indicates the i-th performance evaluation indicator in the main index set; Indicates the data value to be evaluated corresponding to the i-th performance evaluation indicator in the main index set; Indicates the first weight corresponding to the i-th performance evaluation indicator in the main index set; Indicates the influence factor of the remaining n - 1 performance evaluation indicators in the main index set except the i-th performance evaluation indicator on the i-th performance evaluation indicator; Indicates the calculation weight of the effect analysis result corresponding to the secondary index set; Indicates the total number of performance evaluation indicators in the secondary index set; Indicates the j-th performance evaluation indicator in the secondary index set; Indicates the data value to be evaluated corresponding to the j-th performance evaluation indicator in the secondary index set; Indicates the second weight corresponding to the j-th performance evaluation indicator in the secondary index set; Indicates the influence factor of the remaining m - 1 performance evaluation indicators in the secondary index set except the j-th performance evaluation indicator on the j-th performance evaluation indicator.

[0049] On the other hand, the present invention also provides a heterogeneous multi-core coexistence management method for cloud computing, including:

[0050] Step 1: Obtain the device information and performance information of server clusters with multiple different cores in the system, and summarize and output multi-core heterogeneous cluster information;

[0051] Step 2: Based on the multi-core heterogeneous cluster information, use a preset container technology to establish multiple containers, and construct a mapping network between each container and each server cluster;

[0052] Step 3: Based on the multi-core heterogeneous cluster information and the mapping network, establish a unified API interface and middleware, and output cluster intercommunication information;

[0053] Step 4: Obtain the application requirement information of the system and the resource information of each server cluster, output a real-time requirement-resource view, and obtain a resource scheduling strategy and a resource scheduling method that match the real-time requirement-resource view in a policy-method database. Based on the resource scheduling strategy and the resource scheduling method, and in combination with the cluster intercommunication information, allocate the tasks in the application to the nodes under the corresponding server cluster.

[0054] A heterogeneous multi-core coexistence management system and method for cloud computing provided by the present invention realizes the effective management and resource scheduling of a multi-core heterogeneous cluster through the collaborative work of a cluster information acquisition module, a container establishment module, an interconnection establishment module, and a multi-core resource scheduling module to meet the requirements of application programs. The present invention realizes the efficient utilization of resources and the intelligent scheduling of tasks, improves the system performance and stability, simplifies the complexity of management and deployment, and enhances the overall operation and maintenance efficiency and user experience. Through the optimization of containerization technology and resource scheduling strategies, the heterogeneous multi-core cloud platform can better adapt to the requirements of different application scenarios, improving the flexibility and scalability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0056] Figure 1 It is a schematic framework diagram of a heterogeneous multi-core coexistence management system for cloud computing provided by an embodiment of the present invention;

[0057] Figure 2 It is a schematic flowchart of a heterogeneous multi-core coexistence management method for cloud computing provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0059] As Figure 1 shown, a heterogeneous multi-core coexistence management system for cloud computing provided by an embodiment of the present invention includes:

[0060] A cluster information acquisition module, configured to acquire the device information and performance information of server clusters with multiple different cores in the system, and summarize and output multi-core heterogeneous cluster information;

[0061] A container establishment module, configured to establish multiple containers based on the multi-core heterogeneous cluster information by using a preset container technology, and construct a mapping network between each container and each server cluster;

[0062] An interconnection establishment module, which is used to establish a unified API interface and middleware based on multi-core heterogeneous cluster information and a mapping network, and output cluster interconnection information;

[0063] A multi-core resource scheduling module, which is used to obtain the application requirement information of the system and the resource information of each server cluster, output a real-time requirement-resource view, and obtain a resource scheduling policy and a resource scheduling method that match the real-time requirement-resource view in a policy-method database. Based on the resource scheduling policy and the resource scheduling method, and combined with the cluster interconnection information, tasks in the application are assigned to nodes under the corresponding server cluster.

[0064] In this embodiment, device information: information related to the hardware devices of multiple server clusters with different cores, such as model, configuration, status, etc.;

[0065] In this embodiment, performance information: performance parameters of multiple server clusters, such as CPU utilization rate, memory utilization rate, network bandwidth, etc.;

[0066] In this embodiment, multi-core heterogeneous cluster information: summarizes the device information and performance information of multiple server clusters with different cores in the system, and is used for overall management and scheduling;

[0067] In this embodiment, a preset container technology: refers to a pre-set containerization technology used to achieve application isolation and deployment;

[0068] In this embodiment, a mapping network: that is, the mapping relationship between containers and each server cluster, ensuring that containers can correctly access the required resources;

[0069] In this embodiment, a unified API interface: provides a unified interface, enabling different types of server clusters to communicate and operate with the system in the same way;

[0070] In this embodiment, middleware: a communication bridge used to connect different server clusters and containers, providing functions for data transmission and processing, including but not limited to, message queues (such as RabbitMQ, Kafka), message middleware (such as ActiveMQ, RabbitMQ), data middleware (such as Redis), ESB (Enterprise Service Bus), etc.;

[0071] In this embodiment, cluster interconnection information: information including the communication methods and rules between server clusters, for example, a unified API interface and middleware, to ensure that clusters can communicate and cooperate with each other;

[0072] In this embodiment, application requirement information: describes the requirements of application programs in the system, including resource requirements, operating environment, etc.;

[0073] In this embodiment, resource information: describes the resource situation of each server cluster, including resources such as available CPUs, memory, and storage;

[0074] In this embodiment, real-time demand-resource view: a real-time view generated based on application demand information and resource information, used for resource scheduling decision-making;

[0075] In this embodiment, policy-method database: stores various resource scheduling policies and methods, used to select appropriate resource scheduling policies and methods according to the real-time demand-resource view;

[0076] In this embodiment, resource scheduling policy: describes the decision rules and algorithms for resource scheduling, used to allocate resources according to real-time demands;

[0077] In this embodiment, resource scheduling method: the specific implementation methods of resource scheduling, including methods such as task allocation and load balancing.

[0078] The implementation principle and beneficial effects of this embodiment: Through the collaborative work of cluster information acquisition, container establishment, interconnection establishment, and multi-core resource scheduling module, the present invention realizes the effective management and resource scheduling of multi-core heterogeneous clusters to meet the requirements of application programs. The present invention realizes the efficient utilization of resources and the intelligent scheduling of tasks, improves the system performance and stability, simplifies the complexity of management and deployment, and enhances the overall operation and maintenance efficiency and user experience. Also, through the optimization of containerization technology and resource scheduling policies, the heterogeneous multi-core cloud platform can better adapt to the requirements of different application scenarios, improving the flexibility and scalability of the system.

[0079] A heterogeneous multi-core coexistence management system for cloud computing provided by an embodiment of the present invention, the cluster information acquisition module includes:

[0080] Cluster category information acquisition sub-module, used to acquire the type information of each server cluster in the system and output cluster category information;

[0081] Chip-device-performance information acquisition sub-module, used to acquire the chip environment information corresponding to each type of server cluster in the cluster category information, as well as the device information and performance information under the corresponding chip environment, and summarize and output multi-core heterogeneous cluster information.

[0082] In this embodiment, type information: describes the types or categories of each server cluster in the system, for example, high-performance computing clusters, distributed storage clusters, virtualization clusters, etc.;

[0083] In this embodiment, cluster category information: refers to the information for classifying and identifying different types of server clusters, used to distinguish various types of clusters;

[0084] In this embodiment, the chip environment information refers to the environment information of a specific chip or processor, including the supported instruction set, architecture features, performance parameters, etc. The chip environment information is very important for system resource management and scheduling because different chip environments may require different optimization strategies and resource allocation methods.

[0085] The implementation principle and beneficial effects of this embodiment: Through the collaborative work of the cluster category information acquisition sub-module and the chip-device-performance information acquisition sub-module of the present invention, the system can accurately identify various types of server clusters, obtain their corresponding chip environment information and performance data, and thus construct multi-core heterogeneous cluster information. The present invention can achieve more refined resource management and scheduling according to different types of server clusters and chip environment information, improve the performance and efficiency of the system. At the same time, through the accurate grasp of the cluster category and chip environment, it is possible to better optimize resource allocation, improve the running efficiency of application programs, and reduce energy consumption and costs. This refined management and scheduling ability can bring better performance and user experience to the cloud computing environment.

[0086] A heterogeneous multi-core coexistence management system for cloud computing provided by an embodiment of the present invention, the container establishment module includes:

[0087] A feature extraction sub-module, which is used to extract features from the multi-core heterogeneous cluster information and construct a first feature set based on the extracted features;

[0088] A container construction sub-module, which is used to select at least two preset container technologies that match the first feature set from the method database in combination with a preset feature-technology mapping table, and establish multiple containers based on the preset container technologies;

[0089] A network construction sub-module, which is used to establish a mapping network between each container and each server cluster.

[0090] In this embodiment, the first feature set: is a set of key features extracted from the multi-core heterogeneous cluster information and used as a reference for subsequent container construction;

[0091] In this embodiment, the preset feature-technology mapping table: describes the mapping relationship between features and container technologies, which is preset in advance and can help the system select appropriate container technologies to meet the requirements of specific features;

[0092] In this embodiment, the method database: stores various methods and strategies used in the system, including container construction methods, network construction methods, etc.;

[0093] In this embodiment, the container: is a virtualization technology used to encapsulate an application program and all its runtime environments and dependencies to achieve isolation and deployment of the application program. For example, container technologies such as Docker and Kubernetes.

[0094] Implementation principle and beneficial effects of this embodiment: The present invention extracts key features through the feature extraction sub-module, selects appropriate container technologies through the container construction sub-module, and establishes connections between containers and server clusters through the network construction sub-module, enabling the system to achieve containerized deployment and management. The present invention can improve the flexibility and portability of the system, simplify the deployment and maintenance of application programs, and select appropriate container technologies based on the extracted data features, enabling the system to better adapt to different application scenarios and requirements, improving the efficiency and performance of the system. At the same time, the network construction sub-module ensures smooth communication between containers and server clusters, further enhancing the stability and reliability of the system.

[0095] A heterogeneous multi-core coexistence management system for cloud computing provided by an embodiment of the present invention, an interconnection establishment module, includes:

[0096] An interface information acquisition sub-module, configured to acquire the interface information of each server cluster in the multi-core heterogeneous cluster information and output cluster interface information;

[0097] An interface unification sub-module, configured to configure the API gateways of each server cluster based on the cluster interface information and establish a unified API interface;

[0098] A middleware establishment sub-module, configured to select middleware that matches the middleware function requirement information from the method database based on the acquired middleware function requirement information;

[0099] An interconnection information establishment sub-module, configured to output cluster interconnection information based on the unified API interface and middleware.

[0100] In this embodiment, the cluster interface information refers to the interface information of each server cluster, including communication protocols, data formats, access permissions, etc., and is used to describe the communication methods and rules between servers;

[0101] In this embodiment, the API gateway is a server used to manage, monitor, and protect APIs, and is used to uniformly manage interface access between different platforms or server clusters;

[0102] In this embodiment, the middleware function requirement information refers to the description of the system's functional requirements for middleware, including requirements in aspects such as data processing, message passing, and security. Selecting appropriate middleware based on these requirements can better support the functions and performance of the system.

[0103] Implementation principle and beneficial effects of this embodiment: Through the collaborative work of the interface information acquisition sub-module, interface unification sub-module, middleware establishment sub-module, and intercommunication information establishment sub-module, the system can uniformly manage the interfaces of each server cluster, select appropriate middleware, establish intercommunication channels, and achieve mutual communication and collaboration among the internal modules of the system. The present invention can standardize and uniformly manage the interfaces between server clusters, simplify the communication and integration processes of the system, improve the maintainability and scalability of the system, and also support the system function requirements by selecting appropriate middleware, enhance the performance and efficiency of the system, and at the same time reduce the complexity of system integration and development. The design of this intercommunication establishment module can strengthen the collaboration among the internal modules of the system, and improve the overall operation efficiency and stability of the system.

[0104] A heterogeneous multi-core coexistence management system for cloud computing provided by an embodiment of the present invention, the middleware establishment sub-module includes:

[0105] A requirement acquisition unit, configured to acquire functional requirement information for mutual communication and data exchange between each server cluster;

[0106] A middleware matching unit, configured to select a matching middleware from the method database based on the functional requirement information;

[0107] A middleware configuration and optimization unit, configured to establish the architecture of the middleware, develop and integrate the middleware based on the architecture, and at the same time, perform performance testing on the middleware, and perform performance optimization and function expansion on the middleware in combination with the performance test results and the functional requirement information.

[0108] In this embodiment, the functional requirement information: a description of the functional requirements for mutual communication and data exchange between different server clusters in the system, including but not limited to, data exchange, communication protocols, security requirements, etc. These requirement information can guide the selection and development of middleware;

[0109] In this embodiment, the architecture: refers to the overall design structure of the middleware, including components, interfaces, interaction methods, etc. A good architecture can support the functional requirements of the system and provide good performance;

[0110] In this embodiment, the performance test results: the results obtained after performing performance testing on the middleware, including but not limited to, test results under indicators such as response time, throughput, and concurrent performance;

[0111] In this embodiment, the performance optimization: the optimization work performed on the performance test results, aiming to improve the performance of the middleware, reduce resource occupancy, and improve the response speed and stability of the system;

[0112] In this embodiment, function expansion refers to expanding the middleware on the basis of the original functions to add new functions or support more application scenarios.

[0113] Implementation principle and beneficial effects of this embodiment: Through the collaborative work of the requirement acquisition unit, middleware matching unit, and middleware configuration and optimization unit of the present invention, the system can select appropriate middleware according to functional requirements, design and optimize the middleware architecture to meet the communication and data exchange requirements of the system. The present invention can select the most suitable middleware according to system requirements, improve the performance and stability of the middleware through performance testing and optimization. At the same time, through function expansion, the functions of the middleware can be extended, enabling the heterogeneous multi-core cloud platform to handle more application scenarios and requirements.

[0114] A heterogeneous multi-core coexistence management system for cloud computing provided by an embodiment of the present invention, the multi-core resource scheduling module includes:

[0115] The application requirement acquisition sub-module is used to output the first requirement information based on the requirement information of each application in the system obtained in real time.

[0116] The management requirement acquisition sub-module is used to obtain the unified management requirement information of the system and output the second requirement information.

[0117] The requirement information generation sub-module is used to summarize the first requirement information and the second requirement information and output the cluster requirement information.

[0118] The resource data acquisition sub-module is used to obtain the resource data and the corresponding resource status of each server cluster in the system in real time and output the cluster resource information.

[0119] The view construction sub-module is used to construct a real-time requirement-resource view based on the cluster requirement information and the cluster resource information and using a preset view construction method.

[0120] The requirement-resource matching sub-module is used to match the requirement information and the resource information in the real-time requirement-resource view and output the requirement-resource matching result.

[0121] The factor acquisition sub-module is used to obtain the first screening factor matching the requirement-resource matching result in combination with a preset result-factor comparison table.

[0122] The policy-method matching sub-module is used to select a resource scheduling policy that meets the preset screening conditions and the corresponding resource scheduling method in the policy-method database based on the first screening factor.

[0123] A task allocation sub-module, which is used to allocate various tasks in an application to the node resources under the corresponding server cluster based on a resource scheduling policy and a resource scheduling method, and in combination with cluster interconnection information, and output resource scheduling process data;

[0124] An index acquisition sub-module, which is used to select corresponding performance evaluation indexes in an index database based on the acquired index selection instruction;

[0125] An effect analysis sub-module, which is used to perform effect analysis on the resource scheduling process data based on the performance evaluation indexes and output a first result;

[0126] An optimization analysis sub-module, which is used to perform optimization analysis on the first result by combining the optimization indexes selected in the index database and output an optimization analysis result;

[0127] A policy-method optimization sub-module, which is used to select a preset optimization method that matches the optimization analysis result in a method database, and optimize the resource scheduling policy and the resource scheduling method based on the preset optimization method.

[0128] In this embodiment, the first demand information refers to the real-time demand information of each application in the system, such as the demand for computing resources, storage resources, etc.;

[0129] In this embodiment, the second demand information is the unified management demand information of the system, such as the demand in terms of security, reliability, etc.;

[0130] In this embodiment, the cluster demand information is the cluster-level demand information that integrates application demands and management demands;

[0131] In this embodiment, the cluster resource information includes the resource data and status information of each server cluster in the system, including but not limited to the CPU utilization rate, memory usage, and load conditions of each server cluster, etc.;

[0132] In this embodiment, the preset view construction method is a method for constructing a real-time demand-resource view, which is preset;

[0133] In this embodiment, the demand-resource matching result is the result of the matching between real-time demands and resource information, which matches the demands of the application with available resources to facilitate the determination of the optimal resource allocation plan;

[0134] In this embodiment, the preset result-factor comparison table is a pre-defined table that contains the mapping relationship between the demand-resource matching result and the first screening factor;

[0135] In this embodiment, the first screening factor is a screening factor for the resource scheduling policy and the resource scheduling method selected according to the demand-resource matching result;

[0136] In this embodiment, the preset screening conditions: the preset conditions for screening resource scheduling strategies and methods;

[0137] In this embodiment, the resource scheduling process data: that is, the data generated during the resource scheduling process, including information such as task allocation and resource utilization;

[0138] In this embodiment, the index selection instruction: the instruction for selecting performance evaluation indexes;

[0139] In this embodiment, the index database: the database storing various performance evaluation indexes and optimization indexes;

[0140] In this embodiment, the performance evaluation index: the index for evaluating the resource scheduling effect. For example, response time, throughput, etc.;

[0141] In this embodiment, the first result: that is, the result of evaluating the resource scheduling effect based on the performance evaluation index;

[0142] In this embodiment, the optimization index: the index for guiding resource scheduling optimization. For example, cost reduction, performance improvement, etc.;

[0143] In this embodiment, the optimization analysis result: the result obtained after optimizing and analyzing the resource scheduling effect;

[0144] In this embodiment, the preset optimization method: the method or strategy predefined for optimizing resource scheduling.

[0145] The implementation principle and beneficial effects of this embodiment: Through the collaborative work of each sub-module under the multi-core resource scheduling module, the present invention can make intelligent resource scheduling decisions according to application requirements, management requirements, and in combination with resource conditions, and continuously optimize the scheduling strategy and method through performance evaluation and optimization analysis. The present invention can perform intelligent scheduling according to real-time requirements and resource conditions, improve resource utilization and system performance, and at the same time optimize according to the evaluation results, making resource scheduling more efficient and flexible, thereby enhancing the overall resource utilization, operation efficiency, and user experience of the system.

[0146] A heterogeneous multi-core coexistence management system for cloud computing provided by an embodiment of the present invention, the effect analysis sub-module, includes:

[0147] The index division unit is used to divide the performance evaluation indexes to obtain a main index set and a secondary index set;

[0148] The weight acquisition unit is used to obtain the first weight of each performance evaluation index in the main index set and the second weight of each performance evaluation index in the secondary index set, and output an index-weight comparison table;

[0149] An index relationship acquisition unit, configured to acquire a first relationship between performance evaluation indexes in the main index set, a second relationship between performance evaluation indexes in the secondary index set, and a third relationship between performance evaluation indexes in the main index set and the secondary index set, and construct an index relationship graph;

[0150] A data parsing unit, configured to parse the resource scheduling process data to obtain the data to be evaluated;

[0151] An effect analysis unit, configured to perform effect analysis on the data to be evaluated based on the main index set, the secondary index set, the index-weight comparison table, and the index relationship graph, and obtain an effect analysis result;

[0152] ;

[0153] Wherein, represents the comprehensive effect analysis value based on the main index set and the secondary index set; represents an exponential function; represents the cross-correlation coefficient between the main index set and the secondary index set; represents the calculation weight of the effect analysis result corresponding to the main index set; represents the total number of performance evaluation indexes in the main index set; represents the i-th performance evaluation index in the main index set; represents the value of the data to be evaluated corresponding to the i-th performance evaluation index in the main index set; represents the first weight corresponding to the i-th performance evaluation index in the main index set; represents the influence factor of the remaining n-1 performance evaluation indexes in the main index set except the i-th performance evaluation index on the i-th performance evaluation index; represents the calculation weight of the effect analysis result corresponding to the secondary index set; represents the total number of performance evaluation indexes in the secondary index set; represents the j-th performance evaluation index in the secondary index set; represents the value of the data to be evaluated corresponding to the j-th performance evaluation index in the secondary index set; represents the second weight corresponding to the j-th performance evaluation index in the secondary index set; represents the influence factor of the remaining m-1 performance evaluation indexes in the secondary index set except the j-th performance evaluation index on the j-th performance evaluation index.

[0154] In this embodiment, the main index set: a key index set obtained by dividing performance evaluation indexes, usually used to measure important indexes of system performance, for example, response time, resource utilization rate, throughput, etc.;

[0155] In this embodiment, the secondary index set: corresponding to the primary index set, the secondary index set is a set of indexes that are secondary but still have a certain influence in the performance evaluation indexes. For example, stability, reliability, cost, etc. It should be noted that the division rules between the primary index set and the secondary index set can be divided according to the specific performance requirements in the actual situation;

[0156] In this embodiment, the first weight: the weight coefficient for each performance evaluation index in the primary index set. For example, the response time may be given a higher weight because it has a more direct impact on the user experience;

[0157] In this embodiment, the second weight: the weight coefficient for each performance evaluation index in the secondary index set. These weights are usually relatively low, but still have a certain impact on the overall system effect;

[0158] In this embodiment, the index-weight comparison table: comparing each performance evaluation index with its corresponding weight for use in the subsequent effect analysis process;

[0159] In this embodiment, the first relationship: that is, the correlation relationship between the performance evaluation indexes within the primary index set;

[0160] In this embodiment, the second relationship: that is, the correlation relationship between the performance evaluation indexes within the secondary index set;

[0161] In this embodiment, the third relationship: that is, the correlation relationship between the performance evaluation indexes between the primary index set and the secondary index set;

[0162] In this embodiment, the index relationship diagram: a graphical representation constructed based on the primary index set, the secondary index set, and the relationship between them, used to show the correlation and influence degree between each performance evaluation index;

[0163] In this embodiment, the data to be evaluated: the data that needs to be analyzed for its effect after being processed by the data parsing unit, including the data corresponding to various performance evaluation indexes generated during the resource scheduling process and to be evaluated and analyzed.

[0164] The implementation principle and beneficial effects of this embodiment: By allocating weights to the primary index set and the secondary index set, modeling the index relationships, and parsing the data, and combining with the formula to calculate the comprehensive effect analysis value E, the effect of resource scheduling is evaluated. The present invention can help system administrators or decision-makers more comprehensively understand the effect of resource scheduling. By quantitatively analyzing the relationships and weights between indexes, it can comprehensively consider the importance and mutual relationships of each index, improve the accuracy of the resource scheduling effect analysis results, provide data support for the optimization of subsequent scheduling strategies and methods, and thus improve the resource utilization efficiency of the system.

[0165] Such as Figure 2As shown, a heterogeneous multi-core coexistence management method for cloud computing provided by an embodiment of the present invention includes:

[0166] Step 1: Obtain the device information and performance information of server clusters with multiple different cores in the system, and summarize and output multi-core heterogeneous cluster information;

[0167] Step 2: Based on the multi-core heterogeneous cluster information, use a preset container technology to establish multiple containers, and construct a mapping network between each container and each server cluster;

[0168] Step 3: Based on the multi-core heterogeneous cluster information and the mapping network, establish a unified API interface and middleware, and output cluster interoperability information;

[0169] Step 4: Obtain the application requirement information of the system and the resource information of each server cluster, output a real-time requirement-resource view, and obtain a resource scheduling policy and a resource scheduling method that match the real-time requirement-resource view in a policy-method database. Based on the resource scheduling policy and the resource scheduling method, and combined with the cluster interoperability information, allocate the tasks in the application to the nodes under the corresponding server cluster.

[0170] Implementation principle and beneficial effects of this embodiment: Through the collaborative work of the cluster information acquisition, container establishment, interoperability establishment, and multi-core resource scheduling modules, the present invention realizes the effective management and resource scheduling of multi-core heterogeneous clusters to meet the requirements of application programs. The present invention realizes the efficient utilization of resources and the intelligent scheduling of tasks, improves the system performance and stability, simplifies the complexity of management and deployment at the same time, and improves the overall operation and maintenance efficiency and user experience. Also, through the optimization of containerization technology and resource scheduling policies, the heterogeneous multi-core cloud platform can better adapt to the requirements of different application scenarios, improving the flexibility and scalability of the system.

[0171] Finally, it should be noted that: The above embodiments are only used to illustrate the technical solutions of the present invention, not to limit them; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A heterogeneous multi-core coexistence management system for cloud computing, characterized in that: include: The cluster information acquisition module is used to obtain the device information and performance information of multiple server clusters with different cores in the system, and summarize and output the multi-core heterogeneous cluster information; A container establishment module, used to establish multiple containers based on the multi-core heterogeneous cluster information using a preset container technology, and to construct a mapping network between each of the containers and each server cluster; An intercommunication establishment module, used to establish a unified API interface and middleware based on the multi-core heterogeneous cluster information and the mapping network, and output cluster intercommunication information; A multi-core resource scheduling module is used to obtain application demand information of the system and resource information of each server cluster, output a real-time demand-resource view, and obtain a resource scheduling strategy and a resource scheduling method matching the real-time demand-resource view in a strategy-method database, and allocate tasks in the application to nodes under the corresponding server cluster based on the resource scheduling strategy and the resource scheduling method and in combination with the cluster intercommunication information; Wherein, the multi-core resource scheduling module includes: The indicator acquisition submodule is used to select and obtain corresponding performance evaluation indicators in the indicator database based on the obtained indicator selection instruction; The effect analysis submodule is used to perform effect analysis on the resource scheduling process data based on the performance evaluation index and output a first result, including: An indicator division unit, used for dividing the performance evaluation indicators to obtain a main indicator set and a secondary indicator set; A weight acquisition unit, used to acquire a first weight of each performance evaluation indicator in the main indicator set and a second weight of each performance evaluation indicator in the secondary indicator set, and output an indicator-weight comparison table; An indicator relationship acquisition unit, used to acquire a first relationship between each performance evaluation indicator in the main indicator set, a second relationship between each performance evaluation indicator in the secondary indicator set, and a third relationship between each performance evaluation indicator in the main indicator set and the secondary indicator set, and construct an indicator relationship graph; A data parsing unit, used to parse the resource scheduling process data to obtain data to be evaluated; An effect analysis unit, used to perform effect analysis on the data to be evaluated based on the main indicator set, the sub-indicator set, the indicator-weight comparison table and the indicator relationship diagram, to obtain an effect analysis result; ； in, Indicates the comprehensive effect analysis value based on the main indicator set and the sub-indicator set; represents the exponential function; Indicates the mutual correlation coefficient between the main index set and the secondary index set; Indicates the calculation weight of the effect analysis results corresponding to the main indicator set; Represents the total number of performance evaluation indicators in the main indicator set; represents the i-th performance evaluation indicator in the main indicator set; represents the data value to be evaluated corresponding to the i-th performance evaluation indicator in the main indicator set; represents a first weight corresponding to the i-th performance evaluation indicator in the main indicator set; Indicates the influence factors of the remaining n-1 performance evaluation indicators in the main indicator set except the i-th performance evaluation indicator on the i-th performance evaluation indicator; Indicates the calculation weight of the effect analysis result corresponding to the sub-indicator set; Represents the total number of performance evaluation indicators in the sub-indicator set; represents the jth performance evaluation indicator in the sub-indicator set; represents the data value to be evaluated corresponding to the jth performance evaluation indicator in the secondary indicator set; represents a second weight corresponding to the jth performance evaluation indicator in the secondary indicator set; It represents the influence factors of the remaining m-1 performance evaluation indicators in the secondary indicator set except the j-th performance evaluation indicator on the j-th performance evaluation indicator.

2. A heterogeneous multi-core coexistence management system for cloud computing according to claim 1, characterized in that: The cluster information acquisition module includes: The cluster category information acquisition submodule is used to obtain the type information of each server cluster in the system and output the cluster category information; The chip-device-performance information acquisition submodule is used to obtain the chip environment information corresponding to each category of server clusters in the cluster category information and the device information and performance information under the corresponding chip environment, and summarize and output multi-core heterogeneous cluster information.

3. The heterogeneous multi-core coexistence management system for cloud computing according to claim 1, characterized in that: The container establishment module includes: A feature extraction submodule, used to extract features from the multi-core heterogeneous cluster information and construct a first feature set based on the extracted features; A container construction submodule, used to select at least two preset container technologies matching the first feature set from a method database in combination with a preset feature-technology mapping table, and to establish multiple containers based on the preset container technologies; The network construction submodule is used to establish a mapping network between each container and each server cluster.

4. The heterogeneous multi-core coexistence management system for cloud computing according to claim 1, characterized in that: The intercommunication establishment module includes: An interface information acquisition submodule, used to acquire the interface information of each server cluster in the multi-core heterogeneous cluster information and output the cluster interface information; The interface unification submodule is used to configure the API gateway of each server cluster based on the cluster interface information and establish a unified API interface; A middleware establishment submodule is used to select middleware matching the middleware function requirement information from a method database based on the obtained middleware function requirement information; The intercommunication information establishment submodule is used to output cluster intercommunication information based on the unified API interface and middleware.

5. A heterogeneous multi-core coexistence management system for cloud computing according to claim 4, characterized in that: The middleware establishes a submodule, including: A demand acquisition unit, used to acquire functional demand information for mutual communication and data exchange between server clusters; A middleware matching unit, configured to select a matching middleware from a method database based on the functional requirement information; The middleware configuration and optimization unit is used to establish the architecture of the middleware, and develop and integrate the middleware based on the architecture. At the same time, the middleware is performance tested, and the performance optimization and function expansion of the middleware are performed in combination with the performance test results and the function requirement information.

6. The heterogeneous multi-core coexistence management system for cloud computing according to claim 1, characterized in that: The multi-core resource scheduling module further includes: An application demand acquisition submodule, configured to output first demand information based on demand information of each application in the system acquired in real time; The management requirement acquisition submodule is used to acquire the unified management requirement information of the system and output the second requirement information; A demand information generating submodule, configured to aggregate the first demand information and the second demand information and output cluster demand information; The resource data acquisition submodule is used to obtain the resource data and corresponding resource status of each server cluster in the system in real time, and output cluster resource information; A view construction submodule, used to construct a real-time demand-resource view based on the cluster demand information and cluster resource information and using a preset view construction method; A demand-resource matching submodule, used to match demand information and resource information in the real-time demand-resource view, and output a demand-resource matching result; A factor acquisition submodule, used to acquire a first screening factor matching the demand-resource matching result in combination with a preset result-factor comparison table; A strategy-method matching submodule, used for selecting a resource scheduling strategy that meets a preset screening condition and a resource scheduling method corresponding to the resource scheduling strategy from a strategy-method database based on the first screening factor; A task allocation submodule is used to allocate various tasks in the application to the node resources under the corresponding server cluster based on the resource scheduling strategy and resource scheduling method and in combination with the cluster intercommunication information, and output resource scheduling process data; An optimization analysis submodule, used to perform optimization analysis on the first result in combination with the optimization index selected in the index database, and output the optimization analysis result; The strategy-method optimization submodule is used to select a preset optimization method that matches the optimization analysis result from the method database, and optimize the resource scheduling strategy and resource scheduling method based on the preset optimization method.

7. A heterogeneous multi-core coexistence management method for cloud computing, characterized in that: include: Step 1: Obtain the device information and performance information of multiple server clusters with different cores in the system, and summarize and output the multi-core heterogeneous cluster information; Step 2: Based on the multi-core heterogeneous cluster information, multiple containers are established using a preset container technology, and a mapping network between each container and each server cluster is constructed; Step 3: Based on the multi-core heterogeneous cluster information and the mapping network, a unified API interface and middleware are established to output cluster intercommunication information; Step 4: Obtain application demand information of the system and resource information of each server cluster, output a real-time demand-resource view, and obtain a resource scheduling strategy and a resource scheduling method matching the real-time demand-resource view in the strategy-method database, and assign tasks in the application to nodes under the corresponding server cluster based on the resource scheduling strategy and the resource scheduling method and in combination with the cluster intercommunication information; Wherein, step 4 includes: Based on the obtained indicator selection instruction, a corresponding performance evaluation indicator is selected from the indicator database; Based on the performance evaluation index, the resource scheduling process data is analyzed for effects, and a first result is output, which specifically includes: Dividing the performance evaluation indicators to obtain a main indicator set and a secondary indicator set; Obtaining a first weight of each performance evaluation indicator in the main indicator set and a second weight of each performance evaluation indicator in the secondary indicator set, and outputting an indicator-weight comparison table; Acquire a first relationship between each performance evaluation indicator in the main indicator set, a second relationship between each performance evaluation indicator in the secondary indicator set, and a third relationship between each performance evaluation indicator in the main indicator set and the secondary indicator set, and construct an indicator relationship diagram; Parsing the resource scheduling process data to obtain data to be evaluated; Based on the main indicator set, the sub-indicator set, the indicator-weight comparison table and the indicator relationship diagram, an effect analysis is performed on the data to be evaluated to obtain an effect analysis result; ； in, Indicates the comprehensive effect analysis value based on the main indicator set and the sub-indicator set; represents the exponential function; Indicates the mutual correlation coefficient between the main index set and the secondary index set; Indicates the calculation weight of the effect analysis results corresponding to the main indicator set; Represents the total number of performance evaluation indicators in the main indicator set; represents the i-th performance evaluation indicator in the main indicator set; represents the data value to be evaluated corresponding to the i-th performance evaluation indicator in the main indicator set; represents a first weight corresponding to the i-th performance evaluation indicator in the main indicator set; Indicates the influence factors of the remaining n-1 performance evaluation indicators in the main indicator set except the i-th performance evaluation indicator on the i-th performance evaluation indicator; Indicates the calculation weight of the effect analysis result corresponding to the sub-indicator set; Represents the total number of performance evaluation indicators in the sub-indicator set; represents the jth performance evaluation indicator in the sub-indicator set; represents the data value to be evaluated corresponding to the jth performance evaluation indicator in the secondary indicator set; represents a second weight corresponding to the jth performance evaluation indicator in the secondary indicator set; It represents the influence factors of the remaining m-1 performance evaluation indicators in the secondary indicator set except the j-th performance evaluation indicator on the j-th performance evaluation indicator.

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