Service deployment method and device for cloud resources, equipment, storage medium and product

Abstract

The invention discloses a service deployment method and device for cloud resources, equipment, a storage medium and a product, relates to the technical field of network resource deployment, and discloses a service deployment method for the cloud resources, which comprises the following steps: acquiring demand information of a to-be-deployed service and resource information of each resource node; based on the demand information and the resource information, performing affinity matching through a preset affinity matching algorithm to obtain a target affinity matching result between a to-be-deployed service and a resource node, the target affinity matching result comprising a first resource node satisfying the demand information of the to-be-deployed service; and based on a preset resource optimization rule, performing optimization screening on the first resource node to obtain a second resource node, and deploying the to-be-deployed service to the second resource node. According to the method, the service is deployed to the second resource node, so that the service is deployed on the resource with the optimal performance in each time of service deployment, and the overall utilization rate of the resource is improved.

Description

Technical Field

[0001] The present application relates to the technical field of network resource deployment, and in particular to a method, device, equipment, storage medium and product for business deployment of cloud resources. Background Art

[0002] At present, with the development of cloud computing technology, more and more users are using cloud servers, which means that enterprises usually need to deploy a large number of server nodes to provide corresponding cloud services. Therefore, how to manage the resource deployment of a large number of server nodes is a technical problem that urgently needs to be solved.

[0003] In related technologies, services are usually deployed on adjacent or similar resources by collecting indicator data such as data flow, response time, and collaboration relationship between cloud resource infrastructure nodes, making decisions based on set rules. However, this method deploys services only on adjacent or similar resources by setting rules, which will cause some resources to be overloaded while other resources are idle, resulting in low overall resource utilization.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a business deployment method for cloud resources, aiming to solve the technical problem of low overall resource utilization.

[0006] To achieve the above objectives, the present application proposes a cloud resource service deployment method, the cloud resource service deployment method comprising:

[0007] Obtain demand information of the services to be deployed and resource information of each resource node;

[0008] Based on the demand information and the resource information, affinity matching is performed by a preset affinity matching algorithm to obtain a target affinity matching result between the service to be deployed and the resource node, wherein the target affinity matching result includes a first resource node that meets the demand information of the service to be deployed;

[0009] Based on a preset resource optimization rule, the first resource node is optimized and screened to obtain a second resource node, and the to-be-deployed service is deployed to the second resource node.

[0010] Optionally, the step of performing affinity matching based on the demand information and the resource information by using a preset affinity matching algorithm to obtain a target affinity matching result between the service to be deployed and the resource node includes:

[0011] Based on the preset multi-dimensional data model, the demand information and the resource information, affinity matching is performed by using a preset affinity matching algorithm to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node;

[0012] Based on the multi-dimensional affinity matching results, the best affinity matching result is selected, and the best affinity matching result is used as the target affinity matching result.

[0013] Optionally, the multidimensional data model includes a service feature model, a network model, a service classification model, and a service data model. The step of performing affinity matching based on the preset multidimensional data model, the demand information, and the resource information through a preset affinity matching algorithm to obtain a multidimensional affinity matching result between the service to be deployed and each resource node includes:

[0014] Based on the preset service feature model, the demand information and the resource information, a first affinity matching calculation is performed by a preset feature similarity matching algorithm to obtain a first affinity matching result between the service to be deployed and the resource node;

[0015] Based on a preset network model, the demand information and the resource information, a second affinity matching calculation is performed by a preset node matching algorithm to obtain a second affinity matching result between the service to be deployed and the resource node, wherein the network model includes a plurality of nodes, the network model reflects the connection relationship between the nodes, and the nodes include service nodes and resource nodes;

[0016] Based on the preset service classification model, the demand information and the resource information, a third affinity matching calculation is performed through a preset model evaluation matching algorithm to obtain a third affinity matching result between the service to be deployed and the resource node;

[0017] Based on the preset business data model, the demand information and the resource information, a fourth affinity matching calculation is performed by a preset predictive affinity matching algorithm to obtain a fourth affinity matching result between the business to be deployed and the resource node;

[0018] The first affinity matching result, the second affinity matching result, the third affinity matching result, and the fourth affinity matching result are combined to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node.

[0019] Optionally, the step of performing a first affinity matching calculation based on a preset service feature model, the demand information, and the resource information by a preset feature similarity matching algorithm to obtain a first affinity matching result between the service to be deployed and the resource node includes:

[0020] Acquire the business rules of the business to be deployed, and determine the weight value and target matching scheme of the business to be deployed based on the business rules, wherein the target matching scheme is a scheme for calculating affinity matching results;

[0021] Based on a preset service feature model, feature extraction is performed on the demand information and the resource information respectively to obtain the demand feature of the service to be deployed and the characteristic index feature of each resource node;

[0022] Based on the weight value, a cosine similarity calculation is performed on the demand feature and the characteristic index feature to obtain a first similarity value;

[0023] Through the target matching scheme, the first similarity value is converted into a matching value between the demand characteristics and the characteristic indicator characteristics, and based on the matching value, a first affinity matching result between the service to be deployed and the resource node is generated, wherein the matching value reflects the degree of matching between the demand characteristics of the service to be deployed and the characteristic indicator characteristics of each resource node.

[0024] Optionally, the step of performing a second affinity matching calculation based on a preset network model, the demand information, and the resource information by a preset node matching algorithm to obtain a second affinity matching result between the service to be deployed and the resource node includes:

[0025] Based on a preset network model, determine the first adjacent node of the service node corresponding to the service to be deployed and the second adjacent node of each of the first adjacent nodes, wherein the network model includes each service node and the association relationship between each service node, the service node corresponding to the service to be deployed refers to the service node corresponding to the service to be deployed in the network model, the first adjacent node refers to all nodes in the network model that have a node adjacent relationship with the service node corresponding to the service to be deployed, and the second adjacent node refers to all nodes in the network model that have a node adjacent relationship with the first adjacent node;

[0026] Performing cosine similarity calculation on the demand information corresponding to the service node and the resource information corresponding to each of the second adjacent nodes to obtain a second similarity value between the service node and each of the second adjacent nodes;

[0027] The second adjacent node corresponding to the highest similarity value in the second similarity values ​​is taken as the target matching node, and the target matching node is matched with the service node to obtain a node mapping result, and based on the node mapping result, a second affinity matching result between the service to be deployed and the resource node is generated.

[0028] Optionally, the resource scheduling rule includes a capacity rule and a resource allocation rule, and the step of optimizing and screening the first resource node based on a preset resource optimization rule to obtain the second resource node includes:

[0029] Based on the capacity rule, calculating the predicted resource capacity of each of the first resource nodes after service deployment;

[0030] Based on the resource allocation rule, calculating the resource allocation amount of each of the first resource nodes;

[0031] Based on the predicted resource capacity and the resource allocation amount, an optimal resource node is selected from the first resource nodes through a preset multi-objective optimization algorithm, and the optimal resource node is used as the second resource node, wherein the multi-objective optimization algorithm is an optimization algorithm with the predicted resource capacity and the resource allocation amount as optimization objectives.

[0032] In addition, to achieve the above purpose, the present application also proposes a cloud resource service deployment device, the cloud resource service deployment device comprising:

[0033] The acquisition module is used to obtain the demand information of the service to be deployed and the resource information of each resource node;

[0034] A matching module, configured to perform affinity matching based on the demand information and the resource information through a preset affinity matching algorithm to obtain a target affinity matching result between the service to be deployed and the resource node, wherein the target affinity matching result includes a first resource node that meets the demand information of the service to be deployed;

[0035] The optimization module is used to optimize and screen the first resource node based on a preset resource optimization rule to obtain a second resource node, and deploy the to-be-deployed business to the second resource node.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a business deployment device for cloud resources, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the business deployment method for cloud resources as described above.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the business deployment method of cloud resources as described above are implemented.

[0038] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the business deployment method of cloud resources as described above are implemented.

[0039] One or more technical solutions proposed in this application have at least the following technical effects:

[0040] Compared with the related art in which the service is deployed only on adjacent or similar resources by setting rules, which will cause some resources to be overloaded while other resources are idle, thereby resulting in low overall resource utilization, the present application firstly implements affinity matching calculation to screen the resource nodes from the direction of meeting the demand of the service to be deployed, and then obtains the target affinity matching result of the first resource node including the demand information of the service to be deployed, that is, the first resource node is considered to meet the demand conditions of the service to be deployed, and then based on the preset resource optimization rules, the first resource node is optimized and screened, and finally the second resource node is obtained. It can be understood that the second resource node is further optimized and screened from the perspective of resource performance, that is, the second resource node is considered to meet the resource performance conditions. Based on the above demand conditions for meeting the service to be deployed and the resource screening considering meeting the resource performance conditions, the second resource node obtained in this way is the resource with the best performance deployed by the service to be deployed. Therefore, the deployment of the service to be deployed to the second resource node realizes that each service deployment is to deploy the service on the resource with the best performance, thereby improving the overall resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0043] Figure 1 A flowchart of the first embodiment of the cloud resource service deployment method of the present application is provided;

[0044] Figure 2 A schematic diagram of the business deployment system structure for this application cloud resource;

[0045] Figure 3 A schematic diagram of the business affinity analysis module under the business deployment system of the cloud resources for this application;

[0046] Figure 4 A schematic diagram of a resource analysis model in the business deployment method of cloud resources in this application;

[0047] Figure 5 This is a schematic diagram of a resource provisioning and deployment module in the business deployment method of cloud resources in this application;

[0048] Figure 6 A schematic diagram of the module structure of a service deployment device for cloud resources in an embodiment of the present application;

[0049] Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the business deployment method of cloud resources in the embodiment of the present application.

[0050] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0052] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0053] The main solution of the embodiment of the present application is: obtaining demand information of the business to be deployed and resource information of each resource node; based on the demand information and the resource information, affinity matching is performed through a preset affinity matching algorithm to obtain a target affinity matching result between the business to be deployed and the resource node, wherein the target affinity matching result includes a first resource node that meets the demand information of the business to be deployed; based on a preset resource optimization rule, the first resource node is optimized and screened to obtain a second resource node, and the business to be deployed is deployed to the second resource node.

[0054] In this embodiment, for the convenience of description, the following description is made by taking the service deployment device of cloud resources as the execution subject.

[0055] In related technologies, services are usually deployed on adjacent or similar resources by collecting data on indicators such as data flow, response time, and collaboration relationships between cloud resource infrastructure nodes, and making decisions based on set rules. However, this method deploys services only on adjacent or similar resources by setting rules, which will cause some resources to be overloaded while other resources are idle, resulting in low overall resource utilization.

[0056] This application provides a solution to implement the deployment of services on resource nodes and improve the overall resource utilization.

[0057] It can be seen from the above embodiments that the present application first implements the screening of resource nodes from the direction of meeting the demand of the business to be deployed through affinity matching calculation, and then obtains the target affinity matching result of the first resource node including the demand information that meets the business to be deployed, that is, the first resource node considers meeting the demand conditions of the business to be deployed, and then based on the preset resource optimization rules, the first resource node is optimized and screened, and finally the second resource node is obtained. It can be understood that the second resource node is further optimized and screened from the perspective of resource performance, that is, the second resource node considers meeting the resource performance conditions. Based on the above conditions for meeting the demand of the business to be deployed and considering the screening of resources that meet the resource performance conditions, the second resource node obtained in this way is the resource with the best performance deployed by the business to be deployed. Therefore, the deployment of the business to be deployed to the second resource node realizes that each business deployment is to deploy the business on the resource with the best performance, thereby improving the overall resource utilization.

[0058] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a terminal system, etc. The following takes the bastion host as an example to illustrate this embodiment and the following embodiments.

[0059] Based on this, the present application embodiment provides a cloud resource service deployment method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the business deployment method of cloud resources in this application.

[0060] In this embodiment, the cloud resource service deployment method includes steps S100 to S300:

[0061] Step S100, obtaining demand information of the service to be deployed and resource information of each resource node;

[0062] It should be noted that the business to be deployed refers to the business to be deployed to the resource node. The demand information of the business to be deployed includes but is not limited to user needs, functional requirements, performance requirements, etc. The resource node refers to the node of cloud computing resources in the network topology, which is used to deploy and process the business to be deployed. The resource information is not limited to information on resource quantity, specifications, configuration, location, etc.

[0063] In a specific implementation, the business deployment device of cloud resources may obtain demand information of the business to be deployed by receiving demand information of the business to be deployed sent by the user; the business deployment device of cloud resources may obtain resource information of each resource node by receiving device-related information sent by the computing device of each resource node, and determining the resource information of the resource node based on the device-related information.

[0064] In the specific implementation, the system designed by this application mainly includes three parts: portal, business affinity analysis module, and resource deployment module. Through core modules such as business configuration, business matching algorithm, intelligent resource scheduling, and elastic capacity estimation, the resource deployment of business affinity is realized. The overall architecture of the system is as follows: Figure 2 As shown, the main implementation steps are as follows:

[0065] Business requirements analysis: Detailed analysis of business requirements, including user requirements, functional requirements, performance requirements, etc.;

[0066] Business affinity analysis: Use the business matching algorithm to calculate the cosine similarity between the feature vectors of business requirements and resource characteristic indicators to determine their affinity;

[0067] Resource provisioning planning: Develop resource provisioning plans based on business needs and affinity analysis results, including information on resource quantity, specifications, configuration, location, etc.;

[0068] Resource deployment: Deploy and configure resources according to resource activation plan;

[0069] Real-time monitoring and adjustment: Establish a real-time monitoring system to monitor resource utilization, business load, etc. in real time, and dynamically adjust resources as needed.

[0070] Step S200, based on the demand information and the resource information, affinity matching is performed by a preset affinity matching algorithm to obtain a target affinity matching result between the service to be deployed and the resource node, wherein the target affinity matching result includes a first resource node that meets the demand information of the service to be deployed;

[0071] In a specific implementation, the affinity matching refers to the matching between the resources of the resource node and the needs of the business to be deployed. The closer the resources of the resource node are to or meet the needs of the business to be deployed, the higher the affinity matching degree between the two. On the contrary, the closer the resources of the resource node are to or cannot meet the needs of the business to be deployed, the lower the affinity matching degree between the two.

[0072] It should be noted that the target affinity matching result includes the first resource node that meets the demand information of the business to be deployed, that is, the first resource node is a resource node that meets the demand conditions of the business to be deployed, wherein the first resource node that meets the demand conditions of the business to be deployed may be the resource node that is closest to or meets the most demand conditions of the business to be deployed among all resource nodes.

[0073] In the specific implementation, refer to Figure 3The business affinity analysis module in the business affinity resource deployment system mainly includes three parts: business configuration management, business matching algorithm, and analysis report. The business affinity is composed of the following: (1) Business configuration management: Collect and analyze the demand information of each business, including functional requirements, performance requirements, security requirements, etc. By sorting and classifying the requirements, systematically extract business characteristics and indicators to provide a data basis for affinity analysis. (2) Business matching algorithm: Based on the business requirements and resource characteristic indicator data, deeply analyze the matching degree between business and resources, and output the resource combination that best matches the business requirements by comparing and evaluating the advantages and disadvantages of different resource configuration schemes.

[0074] In the specific implementation, affinity matching includes the following beneficial effects: 1. Improve resource utilization: By achieving business affinity, resources can be better and more rationally utilized, resource allocation and recycling can be better managed, and resource utilization can be improved. 2. Simplify management and maintenance: Business affinity can reduce the complexity of resource management and maintenance. Related businesses are deployed on adjacent resources, and can be centrally managed through unified management strategies and tools, simplifying operation and maintenance work. 3. Improve business flexibility: By achieving business affinity, businesses can be adjusted and managed more flexibly. Improve the agility and flexibility of the business to meet rapidly changing business needs.

[0075] In a specific implementation, the cloud resource service deployment device performs affinity matching based on the demand information and the resource information through a preset affinity matching algorithm to obtain a target affinity matching result between the service to be deployed and the resource node, including:

[0076] Based on the preset multi-dimensional data model, the demand information and the resource information, affinity matching is performed through a preset affinity matching algorithm to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node; based on the multi-dimensional affinity matching result, the optimal affinity matching result is selected, and the optimal affinity matching result is used as the target affinity matching result.

[0077] It should be noted that, based on the affinity matching of demand information and resource information, the present application further proposes to perform affinity matching between demand information and resource information in multiple dimensions according to demand information and resource information, and perform affinity matching between demand information and resource information in each dimension. For example, the demand information is A and the resource information is B. In the first dimension, the demand information includes A1, and the resource information includes B1; in the second dimension, the demand information includes A2, and the resource information includes B2; the business deployment device of the cloud resource calculates the affinity matching result X1 between A1 and B1, and the affinity matching result X2 between A2 and B2, respectively, and finally comprehensively considers X1 and X2, selects the optimal affinity matching result X2, and uses the optimal affinity matching result X2 as the target affinity matching result.

[0078] In the specific implementation, the dimensions of demand information and resource information include but are not limited to feature dimensions, category dimensions, network topology dimensions, and business data dimensions. For the affinity matching between demand information and resource information of different dimensions, corresponding multi-dimensional data models, business rules, and logical algorithms are used to calculate the multi-dimensional affinity matching results between the business to be deployed and each resource node. That is, through the business affinity matching of different dimensions, a more comprehensive affinity matching is achieved, preventing the inaccuracy caused by the affinity matching of a single dimension, thereby improving the accuracy of the affinity matching between the business to be deployed and the resource nodes.

[0079] In a specific implementation, the step of performing affinity matching based on a preset multi-dimensional data model, the demand information, and the resource information through a preset affinity matching algorithm to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node includes:

[0080] Based on the preset business feature model, the demand information and the resource information, a first affinity matching calculation is performed by a preset feature similarity matching algorithm to obtain a first affinity matching result between the business to be deployed and the resource node; based on the preset network model, the demand information and the resource information, a second affinity matching calculation is performed by a preset node matching algorithm to obtain a second affinity matching result between the business to be deployed and the resource node, wherein the network model includes a plurality of nodes, the network model reflects the connection relationship between the nodes, and the nodes include business nodes and resource nodes; based on the preset business classification model, the demand information The method comprises the following steps: performing a third affinity matching calculation on the service to be deployed and the resource node based on the preset business data model, the demand information and the resource information, and obtaining a third affinity matching result between the service to be deployed and the resource node; performing a fourth affinity matching calculation on the service to be deployed and the resource node based on the preset business data model, the demand information and the resource information, and obtaining a fourth affinity matching result between the service to be deployed and the resource node; merging the first affinity matching result, the second affinity matching result, the third affinity matching result and the fourth affinity matching result to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node.

[0081] In the specific implementation, this application proposes that in order to realize business affinity analysis, resource feature modeling must also be performed based on business needs, referring to Figure 4The cloud resource model in this application includes but is not limited to a business feature model, a network model, a business classification model, and a business data model. The above models correspond to the following four points respectively: 1. Calculation and analysis based on business features; 2. Analysis based on business direct network models; 3. Classification based on business needs; 4. In-depth mining and analysis of business data through business combination analysis.

[0082] Furthermore, the business feature model is to extract key features and encode them according to business needs, including business type coding, user demand coding, geographic location coding, etc. The same business features are counted; the network model refers to the establishment of a network model between businesses, in which each business is represented as a node, and the association relationship between businesses is counted; the business classification model is to cluster business data, including business function requirements, business performance requirements, business security requirements, and count the same business classification; the business data model is to analyze business data, mine frequently occurring business combinations, and count the business relevance by discovering the association rules between businesses.

[0083] In the specific implementation, different from the traditional affinity management method, the business affinity in this application is analyzed based on the affinity matching algorithm, and then combined with the multi-dimensional data model to obtain the most reasonable affinity analysis report, thereby improving the accuracy of determining the best performance resource matching.

[0084] In a specific implementation, the multidimensional data model includes a business feature model, a network model, a business classification model and a business data model, and the first affinity matching calculation, the second affinity matching calculation, the third affinity matching calculation and the fourth affinity matching calculation are respectively performed to obtain the first affinity matching results, the second affinity matching results, the third affinity matching results and the fourth affinity matching results of different dimensions. That is, the affinity matching algorithm of the multidimensional data model of the present application realizes multi-dimensional affinity matching, obtains the most reasonable affinity analysis report, and thereby improves the accuracy of determining the best performance resource matching.

[0085] In a specific implementation, the step of performing a first affinity matching calculation based on a preset service feature model, the demand information, and the resource information through a preset feature similarity matching algorithm to obtain a first affinity matching result between the service to be deployed and the resource node includes:

[0086] Obtain the business rules of the business to be deployed, and determine the weight value and target matching scheme of the business to be deployed based on the business rules, wherein the target matching scheme is a scheme for calculating the affinity matching result; based on a preset business feature model, perform feature extraction on the demand information and the resource information respectively to obtain the demand characteristics of the business to be deployed and the characteristic index characteristics of each of the resource nodes; based on the weight value, perform cosine similarity calculation on the demand characteristics and the characteristic index characteristics to obtain a first similarity value; through the target matching scheme, convert the first similarity value into a matching value between the demand characteristics and the characteristic index characteristics, and based on the matching value, generate a first affinity matching result between the business to be deployed and the resource nodes, wherein the matching value reflects the degree of matching between the demand characteristics of the business to be deployed and the characteristic index characteristics of each resource node.

[0087] In a specific implementation, the business rules of the business to be deployed are pre-set relevant rules used to characterize whether the business requirements are met by resources. Specifically, the matching algorithm based on business rules usually uses logical expressions to represent the rules, such as conditional statements in the form of "if-then" IF-THEN. Example: If requirement A meets condition B, then requirement A is considered to match service C. Among them, the rule attribute means that the rule usually consists of two parts, namely the antecedent and the consequent. The antecedent describes the characteristics of the requirement, and the consequent describes the characteristics of the service. Example: IF (requirement characteristics) THEN (service characteristics).

[0088] In a specific implementation, the business deployment device of cloud resources performs feature extraction on the demand information and the resource information based on a preset business feature model, and obtains the demand features of the business to be deployed and the characteristic indicator features of each resource node, wherein feature extraction refers to extracting the features of resource information and business demand information, and converting them into a form that can be processed by the algorithm, including but not limited to text descriptions, keywords, attributes, etc., and in order to enable the algorithm to process the feature data, it is converted into a vector form.

[0089] In a specific implementation, the cloud resource service deployment device performs cosine similarity calculation on the demand characteristics and the characteristic index characteristics, specifically including: the cloud resource service deployment device performs matching calculation on the characteristics of the service demand and the characteristics of the resource service, and obtains the matching result according to the definition and conditions of the rule. The matching calculation method is cosine similarity, and the formula includes:

[0090] similarity=(A·B) / (||A||×||B||)

[0091] Among them, A and B represent the feature vectors of business requirements and services, · represents the dot product operation of the vectors, and ||A|| and ||B|| represent the modulus lengths of the vectors. The cosine similarity range is between [-1, 1], and the closer the value is to 1, the higher the match degree.

[0092] Weight setting: Some rules may be more important than other rules, so this application proposes to set weights for the rules. The weights can be determined based on expert knowledge, experience or business needs.

[0093] It should be noted that the target matching scheme is a matching method determined by the business deployment device of the cloud resources according to the business rules of the business to be deployed. Specifically, the target matching scheme includes but is not limited to a binary matching scheme and a continuous matching scheme, wherein the binary matching scheme converts the matching result into a binary form according to the matching threshold to indicate whether it matches. Usually a threshold is used, and if it exceeds the threshold, it is considered a match, otherwise it does not match; the continuous matching scheme generates a continuous matching result based on the specific numerical value of the matching. Normalization can be performed according to the needs of specific tasks for comparison and sorting.

[0094] In the specific implementation, the matching algorithm based on business rules of this application is expressed by a non-fixed formula, which is very flexible. Different business requirements will have different rule definitions and matching degree calculation methods. Combinatorial logic expressions are mainly used to express the condition part and conclusion part of the rule, as shown below: IF (demand feature 1 AND demand feature 2 AND ...) THEN (service feature 1 AND service feature 2 AND ...).

[0095] Furthermore, this application collects, processes and analyzes business data, processes the data through calculation models, and finally generates relevant affinity indicators and reports. The results of business affinity analysis are presented in a visual way, such as through intuitive charts, tables, etc., so that system administrators can more intuitively understand the principles and basis of resource allocation, so as to adjust the model and related indicators in time and keep the system model and algorithm evolving.

[0096] In a specific implementation, the cloud resource service deployment device performs a second affinity matching calculation based on a preset network model, the demand information, and the resource information through a preset node matching algorithm to obtain a second affinity matching result between the service to be deployed and the resource node, including:

[0097] Based on a preset network model, determine the first adjacent node of the service node corresponding to the service to be deployed and the second adjacent node of each of the first adjacent nodes, wherein the network model includes each service node and the association relationship between each service node, the service node corresponding to the service to be deployed refers to the service node corresponding to the service to be deployed in the network model, the first adjacent node refers to all nodes in the network model that have a node adjacent relationship with the service node corresponding to the service to be deployed, and the second adjacent node refers to all nodes in the network model that have a node adjacent relationship with the first adjacent node; perform cosine similarity calculation on demand information corresponding to the service node and resource information corresponding to each of the second adjacent nodes to obtain a second similarity value between the service node and each of the second adjacent nodes; use the second adjacent node corresponding to the highest similarity value among the second similarity values ​​as a target matching node, and form a matching mapping between the target matching node and the service node to obtain a node mapping result, and generate a second affinity matching result between the service to be deployed and the resource node based on the node mapping result.

[0098] In the specific implementation, the matching algorithm based on the network model structure includes: 1. Adjacent node matching: According to the connection relationship of nodes in the cloud network, adjacent nodes are regarded as business combinations with higher affinity; 2. Constructing adjacent node graphs: For each graph G1 and G2, the corresponding adjacent node graphs are constructed according to the adjacent relationship between nodes. The adjacent node graph represents the connection relationship between each node and its adjacent nodes; 3. Initializing matching results: Initializing an empty matching map to store the matching relationship between nodes.

[0099] In a specific implementation, the service deployment device of cloud resources determines the first adjacent node of the service node corresponding to the service to be deployed and the second adjacent node of each of the first adjacent nodes based on a preset network model, and then performs cosine similarity calculation on the service node and each of the second adjacent nodes to obtain a second similarity value corresponding to each of the second adjacent nodes. Finally, the service deployment device of cloud resources performs a second affinity matching calculation based on the demand information, the resource information and the second similarity value through a preset node matching algorithm to obtain a second affinity matching result between the service to be deployed and the resource node.

[0100] For example, the specific steps for the business deployment device of cloud resources to traverse each node under the network model include: first, the business deployment device of cloud resources determines the adjacent node set N1 of v1; secondly, for each adjacent node u1 in N1, the business deployment device of cloud resources determines the adjacent node set N2 of u1 in G2; finally, the business deployment device of cloud resources calculates the similarity between v1 and each node in N2 (a similarity calculation method such as cosine similarity can be used); selects the node u2 with the highest similarity as the matching node of u1; based on the matching result, selects the matching node u2 with the highest similarity, adds (v1, u2) to the matching map, and returns the matching map.

[0101] Furthermore, the present application also proposes a matching algorithm based on a business classification model, which calculates the similarity or distance between objects or data and classifies objects or data with high similarity into the same category. The specific steps are as follows:

[0102] Data preprocessing: Preprocess the business objects or data to be matched, such as feature extraction, data cleaning, etc. Feature representation: Represent each business object or data as a feature vector or feature set. The choice of feature vectors depends on the specific problem, and domain knowledge or feature extraction algorithms can be used to determine the key features of the data. Similarity calculation: Calculate the similarity or distance between business objects or data based on their feature vectors. Common similarity calculation methods include cosine similarity, Euclidean distance, Manhattan distance, etc. Category division: Based on similarity or distance, business objects or data with high similarity are divided into the same category. Clustering algorithms (such as K-means clustering, hierarchical clustering) or classification algorithms (such as support vector machines, decision trees) can be used to divide categories. Model evaluation: For the divided categories, model evaluation can be performed to measure the performance of the matching algorithm. Common evaluation indicators include accuracy, recall, F1 value, etc.

[0103] Furthermore, the present application also proposes a matching algorithm based on a business data model: first, the business deployment device of the cloud resource predicts affinity matching, specifically using the affinity scores between businesses predicted by the business data model, and considering the business combination with a higher affinity score as a successful match. The business deployment device of the cloud resource takes the dimension of the business demand feature matrix U as m×k, and the dimension of the business data feature matrix V as n×k, where k is the number of selected potential factors. Then the business demand feature-business data interaction matrix R can be expressed as:

[0104] R≈U×V T

[0105] The goal of matrix decomposition is to minimize the error between the interaction matrix and the approximation matrix, which can also be achieved by minimizing the square error, that is, minimizing the loss function:

[0106] min||RU×V T || 2 .

[0107] It is understandable that the above loss function represents R and U with V T The difference between the products of and is measured by the Euclidean norm (which is the square root of the sum of squares). The sum of squares here represents the square of the difference of each element. In matrix decomposition, the goal is to find matrices U and V such that U×V T As close as possible to the original matrix R, thereby minimizing this loss function.

[0108] Step S300: Based on a preset resource optimization rule, the first resource node is optimized and screened to obtain a second resource node, and the to-be-deployed service is deployed to the second resource node.

[0109] In a specific implementation, the resource optimization rules are preset rules for resource optimization, including but not limited to capacity rules and resource allocation rules. The second resource node obtained by optimizing and screening the first resource node in the present application can be understood as further optimizing and screening the second resource node from the perspective of resource performance, that is, the second resource node is considered to meet the resource performance conditions. Based on the above requirements for meeting the business to be deployed and the resource screening considering meeting the resource performance conditions, the second resource node obtained in this way is the resource with the best performance deployed by the business to be deployed. Therefore, deploying the business to be deployed to the second resource node realizes that each business deployment is to deploy the business on the resource with the best performance, thereby improving the overall resource utilization.

[0110] In a specific implementation, the cloud resource service deployment device optimizes and screens the first resource node based on a preset resource optimization rule to obtain the second resource node, including:

[0111] Based on the capacity rule, the predicted resource capacity of each of the first resource nodes after service deployment is calculated; based on the resource allocation rule, the resource allocation amount of each of the first resource nodes is calculated; based on the predicted resource capacity and the resource allocation amount, through a preset multi-objective optimization algorithm, the optimal resource node is selected from the first resource nodes, and the optimal resource node is used as the second resource node, wherein the multi-objective optimization algorithm is an optimization algorithm with the predicted resource capacity and the resource allocation amount as optimization objectives.

[0112] In the specific implementation, refer to Figure 4,The resource activation and deployment process is based on business rules, including ,intelligent resource scheduling (i.e. resource allocation rules) and elastic capacity ,estimation (i.e. capacity rules). The business deployment device of cloud ,resources adjusts and optimizes the allocation and utilization of cloud resources ,according to actual business needs and rules to ensure efficient operation and ,cost control of the system.

[0113] It should be noted that the multi-objective optimization algorithm is an optimization algorithm used to solve multiple objective functions. The goal of the multi-objective optimization algorithm is to find a balance between multiple potentially conflicting objectives and ultimately obtain an approximate optimal solution set for the multi-objective optimization problem. The multi-objective optimization algorithm includes but is not limited to a genetic algorithm, a particle swarm algorithm, a differential evolution algorithm, and a simulated annealing algorithm.

[0114] In a specific implementation, the present application first calculates the predicted resource capacity and resource allocation amount of each first resource node after service deployment according to the capacity rule and the resource allocation rule, and finally selects the optimal resource node from the first resource nodes through a multi-objective optimization algorithm, and uses the optimal resource node as the second resource node, wherein the multi-objective optimization algorithm is an optimization algorithm with predicted resource capacity and resource allocation amount as optimization objectives.

[0115] Furthermore, the present application proposes a multi-objective optimization algorithm that uses predicted resource capacity and resource allocation as optimization objectives to optimize the first resource node to obtain an optimal resource node. The optimal resource node is the optimal resource node that takes both resource capacity and resource allocation into account, thereby improving the accuracy of determining the best performance resource node.

[0116] Specifically, intelligent resource scheduling: Based on defined business rules, intelligent resource scheduling algorithms are used to allocate and adjust resources. Machine learning-based algorithms are used to perform dynamic scheduling to meet the needs of different business scenarios.

[0117] Elastic capacity estimation: Estimation and forecasting of elastic capacity based on business rules and historical data. This includes analyzing changes in business load so as to adjust resource capacity in a timely manner and ensure that the system can cope with different business situations.

[0118] Flexible rule management: Define flexible rules based on business requirements and system constraints. Use affinity matching algorithms to comprehensively analyze indicators such as business function requirements, performance, and security to provide conditions for rule triggering.

[0119] Capacity estimation and planning: Estimation and planning of elastic capacity is performed based on demand forecasting models and capacity rules. Based on the current resource usage of the business and the expected business growth rate, the capacity changes and expansion plans required in the future are estimated to provide a decision-making basis for business resource adjustments.

[0120] Estimation result display and report: The estimated capacity demand results are presented to system administrators or decision makers in a visual form, including charts, reports, etc. This makes it easier to understand and evaluate the estimation results and make adjustments and decisions in a timely manner.

[0121] Dynamic resource allocation: Combine the forecast results of business resource usage with resource management status to dynamically allocate resources to improve resource utilization and performance.

[0122] Scheduling strategy management: Configure cloud resource scheduling strategies and continuously optimize and improve them to adapt to changes in matching algorithms for business needs and model analysis results in different scenarios, making resource scheduling more reasonable.

[0123] Data analysis and reporting: By analyzing business and cloud resource data and generating reports, calculating and evaluating the effects and benefits of resource scheduling, and continuously optimizing and improving scheduling algorithms and strategies, the rationality of cloud resource deployment and scheduling can be continuously improved.

[0124] Through the above process, intelligent resource scheduling and elastic capacity estimation can be implemented according to business rules, thereby improving resource utilization efficiency, reducing costs, and ensuring system stability and reliability.

[0125] Compared with the related art in which the service is deployed only on adjacent or similar resources by setting rules, which will cause some resources to be overloaded while other resources are idle, thereby resulting in low overall resource utilization, the present application firstly implements affinity matching calculation to screen the resource nodes from the direction of meeting the demand of the service to be deployed, and then obtains the target affinity matching result of the first resource node including the demand information of the service to be deployed, that is, the first resource node is considered to meet the demand conditions of the service to be deployed, and then based on the preset resource optimization rules, the first resource node is optimized and screened, and finally the second resource node is obtained. It can be understood that the second resource node is further optimized and screened from the perspective of resource performance, that is, the second resource node is considered to meet the resource performance conditions. Based on the above demand conditions for meeting the service to be deployed and the resource screening considering meeting the resource performance conditions, the second resource node obtained in this way is the resource with the best performance deployed by the service to be deployed. Therefore, the deployment of the service to be deployed to the second resource node realizes that each service deployment is to deploy the service on the resource with the best performance, thereby improving the overall resource utilization.

[0126] This application also provides a cloud resource service deployment device, please refer to Figure 6 , the cloud resource service deployment device includes:

[0127] The acquisition module 10 is used to acquire the demand information of the service to be deployed and the resource information of each resource node;

[0128] A matching module 20, configured to perform affinity matching based on the demand information and the resource information through a preset affinity matching algorithm to obtain a target affinity matching result between the service to be deployed and the resource node, wherein the target affinity matching result includes a first resource node that meets the demand information of the service to be deployed;

[0129] The optimization module 30 is used to optimize and screen the first resource nodes based on preset resource optimization rules to obtain second resource nodes, and deploy the to-be-deployed business to the second resource nodes.

[0130] Optionally, the matching module 20 includes:

[0131] An affinity matching module, used to perform affinity matching based on a preset multi-dimensional data model, the demand information and the resource information through a preset affinity matching algorithm to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node;

[0132] The selection module is used to select the best affinity matching result based on the multi-dimensional affinity matching results, and use the best affinity matching result as the target affinity matching result.

[0133] Optionally, the affinity matching module includes:

[0134] A first affinity matching calculation module, used to perform a first affinity matching calculation based on a preset service feature model, the demand information and the resource information through a preset feature similarity matching algorithm to obtain a first affinity matching result between the service to be deployed and the resource node;

[0135] A second affinity matching calculation module, configured to perform a second affinity matching calculation based on a preset network model, the demand information, and the resource information by a preset node matching algorithm to obtain a second affinity matching result between the service to be deployed and the resource node, wherein the network model includes a plurality of nodes, the network model reflects the connection relationship between the nodes, and the nodes include service nodes and resource nodes;

[0136] A third affinity matching calculation module is used to perform a third affinity matching calculation based on a preset service classification model, the demand information and the resource information through a preset model evaluation matching algorithm to obtain a third affinity matching result between the service to be deployed and the resource node;

[0137] A fourth affinity matching calculation module, used to perform a fourth affinity matching calculation based on a preset business data model, the demand information and the resource information through a preset predictive affinity matching algorithm to obtain a fourth affinity matching result between the business to be deployed and the resource node;

[0138] A merging module is used to merge the first affinity matching result, the second affinity matching result, the third affinity matching result and the fourth affinity matching result to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node.

[0139] Optionally, the first affinity matching calculation module includes:

[0140] A rule acquisition module, used to acquire the business rules of the business to be deployed, and determine the weight value and target matching scheme of the business to be deployed based on the business rules, wherein the target matching scheme is a scheme for calculating affinity matching results;

[0141] A feature extraction module is used to extract features from the demand information and the resource information based on a preset business feature model, so as to obtain the demand features of the business to be deployed and the characteristic index features of each resource node;

[0142] A similarity calculation module, configured to perform cosine similarity calculation on the demand feature and the characteristic index feature based on the weight value to obtain a first similarity value;

[0143] The first affinity matching result determination module is used to convert the first similarity value into a matching value between the demand characteristics and the characteristic indicator characteristics through the target matching scheme, and generate a first affinity matching result between the service to be deployed and the resource node based on the matching value, wherein the matching value reflects the degree of matching between the demand characteristics of the service to be deployed and the characteristic indicator characteristics of each resource node.

[0144] Optionally, the second affinity matching calculation module includes:

[0145] An adjacent node determination module, used to determine the first adjacent node of the service node corresponding to the service to be deployed and the second adjacent node of each of the first adjacent nodes based on a preset network model, wherein the network model includes each service node and the association relationship between each service node, the service node corresponding to the service to be deployed refers to the service node corresponding to the service to be deployed in the network model, the first adjacent node refers to all nodes in the network model that have a node adjacent relationship with the service node corresponding to the service to be deployed, and the second adjacent node refers to all nodes in the network model that have a node adjacent relationship with the first adjacent node;

[0146] a cosine similarity calculation module, configured to perform cosine similarity calculation on the demand information corresponding to the service node and the resource information corresponding to each of the second adjacent nodes, so as to obtain a second similarity value between the service node and each of the second adjacent nodes;

[0147] The second affinity matching result determination module is used to take the second adjacent node corresponding to the highest similarity value in the second similarity values ​​as the target matching node, and form a matching mapping between the target matching node and the service node to obtain a node mapping result, and based on the node mapping result, generate a second affinity matching result between the service to be deployed and the resource node.

[0148] Optionally, the optimization module 30 includes:

[0149] A predicted resource capacity calculation module, used to calculate the predicted resource capacity of each of the first resource nodes after service deployment based on the capacity rule;

[0150] A resource allocation amount calculation module, configured to calculate the resource allocation amount of each of the first resource nodes based on the resource allocation rule;

[0151] An optimization screening module is used to select the optimal resource node from the first resource nodes based on the predicted resource capacity and the resource allocation amount through a preset multi-objective optimization algorithm, and use the optimal resource node as the second resource node, wherein the multi-objective optimization algorithm is an optimization algorithm with the predicted resource capacity and the resource allocation amount as optimization objectives.

[0152] The cloud resource service deployment device provided by the present application adopts the cloud resource service deployment method in the above embodiment, which can solve the technical problem of cloud resource service deployment. Compared with the prior art, the beneficial effects of the cloud resource service deployment device provided by the present application are the same as the beneficial effects of the cloud resource service deployment method provided by the above embodiment, and other technical features in the cloud resource service deployment device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0153] The present application provides a business deployment device for cloud resources, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the business deployment method for cloud resources in the above-mentioned embodiment one.

[0154] Reference below Figure 7, which shows a schematic diagram of the structure of a cloud resource service deployment device suitable for implementing the embodiments of the present application. The cloud resource service deployment device in the embodiments of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The business deployment device of the cloud resources shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0155] like Figure 7 As shown, the business deployment device of cloud resources may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the business deployment device of cloud resources are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the cloud resource service deployment device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a cloud resource service deployment device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0156] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0157] The cloud resource service deployment device provided by the present application adopts the cloud resource service deployment method in the above embodiment, which can solve the technical problem of cloud resource service deployment. Compared with the prior art, the beneficial effects of the cloud resource service deployment device provided by the present application are the same as the beneficial effects of the cloud resource service deployment method provided by the above embodiment, and other technical features in the cloud resource service deployment device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0158] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0159] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0160] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the business deployment method for cloud resources in the above-mentioned embodiment.

[0161] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0162] The computer-readable storage medium may be included in the service deployment device of the cloud resources; or may exist independently without being assembled into the service deployment device of the cloud resources.

[0163] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the cloud resource service deployment device, the cloud resource service deployment device performs: cloud resource service deployment.

[0164] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0165] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0166] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0167] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned cloud resource service deployment method, and can solve the technical problems of cloud resource service deployment. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the cloud resource service deployment method provided in the above-mentioned embodiment, and will not be elaborated here.

[0168] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned cloud resource business deployment method when executed by a processor.

[0169] The computer program product provided in this application can solve the technical problem of business deployment of cloud resources. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the business deployment method of cloud resources provided in the above embodiment, which will not be repeated here.

[0170] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A cloud resource service deployment method, characterized in that: The method for business deployment of cloud resources includes: Obtain demand information of the services to be deployed and resource information of each resource node; Based on the demand information and the resource information, affinity matching is performed by a preset affinity matching algorithm to obtain a target affinity matching result between the service to be deployed and the resource node, wherein the target affinity matching result includes a first resource node that meets the demand information of the service to be deployed; Based on a preset resource optimization rule, the first resource node is optimized and screened to obtain a second resource node, and the to-be-deployed service is deployed to the second resource node.

2. The cloud resource service deployment method according to claim 1, characterized in that: The step of performing affinity matching based on the demand information and the resource information by using a preset affinity matching algorithm to obtain a target affinity matching result between the service to be deployed and the resource node includes: Based on the preset multi-dimensional data model, the demand information and the resource information, affinity matching is performed by using a preset affinity matching algorithm to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node; Based on the multi-dimensional affinity matching results, the best affinity matching result is selected, and the best affinity matching result is used as the target affinity matching result.

3. The cloud resource service deployment method according to claim 2, characterized in that: The multi-dimensional data model includes a service feature model, a network model, a service classification model and a service data model. The step of performing affinity matching based on the preset multi-dimensional data model, the demand information and the resource information through a preset affinity matching algorithm to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node includes: Based on the preset service feature model, the demand information and the resource information, a first affinity matching calculation is performed by a preset feature similarity matching algorithm to obtain a first affinity matching result between the service to be deployed and the resource node; Based on a preset network model, the demand information and the resource information, a second affinity matching calculation is performed by a preset node matching algorithm to obtain a second affinity matching result between the service to be deployed and the resource node, wherein the network model includes a plurality of nodes, the network model reflects the connection relationship between the nodes, and the nodes include service nodes and resource nodes; Based on the preset service classification model, the demand information and the resource information, a third affinity matching calculation is performed through a preset model evaluation matching algorithm to obtain a third affinity matching result between the service to be deployed and the resource node; Based on the preset business data model, the demand information and the resource information, a fourth affinity matching calculation is performed by a preset predictive affinity matching algorithm to obtain a fourth affinity matching result between the business to be deployed and the resource node; The first affinity matching result, the second affinity matching result, the third affinity matching result, and the fourth affinity matching result are combined to obtain a multi-dimensional affinity matching result between the service to be deployed and each resource node.

4. The cloud resource service deployment method according to claim 3, characterized in that: The step of performing a first affinity matching calculation based on a preset service feature model, the demand information, and the resource information through a preset feature similarity matching algorithm to obtain a first affinity matching result between the service to be deployed and the resource node includes: Acquire the business rules of the business to be deployed, and determine the weight value and target matching scheme of the business to be deployed based on the business rules, wherein the target matching scheme is a scheme for calculating affinity matching results; Based on a preset service feature model, feature extraction is performed on the demand information and the resource information respectively to obtain the demand feature of the service to be deployed and the characteristic index feature of each resource node; Based on the weight value, a cosine similarity calculation is performed on the demand feature and the characteristic index feature to obtain a first similarity value; Through the target matching scheme, the first similarity value is converted into a matching value between the demand characteristics and the characteristic indicator characteristics, and based on the matching value, a first affinity matching result between the service to be deployed and the resource node is generated, wherein the matching value reflects the degree of matching between the demand characteristics of the service to be deployed and the characteristic indicator characteristics of each resource node.

5. The cloud resource service deployment method according to claim 3, characterized in that: The step of performing a second affinity matching calculation based on a preset network model, the demand information, and the resource information by a preset node matching algorithm to obtain a second affinity matching result between the service to be deployed and the resource node includes: Based on a preset network model, determine the first adjacent node of the service node corresponding to the service to be deployed and the second adjacent node of each of the first adjacent nodes, wherein the network model includes each service node and the association relationship between each service node, the service node corresponding to the service to be deployed refers to the service node corresponding to the service to be deployed in the network model, the first adjacent node refers to all nodes in the network model that have a node adjacent relationship with the service node corresponding to the service to be deployed, and the second adjacent node refers to all nodes in the network model that have a node adjacent relationship with the first adjacent node; Performing cosine similarity calculation on the demand information corresponding to the service node and the resource information corresponding to each of the second adjacent nodes to obtain a second similarity value between the service node and each of the second adjacent nodes; The second adjacent node corresponding to the highest similarity value in the second similarity values ​​is taken as the target matching node, and the target matching node is matched with the service node to obtain a node mapping result, and based on the node mapping result, a second affinity matching result between the service to be deployed and the resource node is generated.

6. The cloud resource service deployment method according to claim 1, characterized in that: The resource optimization rule includes a capacity rule and a resource allocation rule. The step of optimizing and screening the first resource node based on the preset resource optimization rule to obtain the second resource node includes: Based on the capacity rule, calculating the predicted resource capacity of each of the first resource nodes after service deployment; Based on the resource allocation rule, calculating the resource allocation amount of each of the first resource nodes; Based on the predicted resource capacity and the resource allocation amount, the optimal resource node is selected from the first resource nodes through a preset multi-objective optimization algorithm, and the optimal resource node is used as the second resource node, wherein the multi-objective optimization algorithm is an optimization algorithm with the predicted resource capacity and the resource allocation amount as optimization objectives.

7. A cloud resource service deployment device, characterized in that: The device comprises: The acquisition module is used to obtain the demand information of the service to be deployed and the resource information of each resource node; A matching module, configured to perform affinity matching based on the demand information and the resource information through a preset affinity matching algorithm to obtain a target affinity matching result between the service to be deployed and the resource node, wherein the target affinity matching result includes a first resource node that meets the demand information of the service to be deployed; The optimization module is used to optimize and screen the first resource node based on a preset resource optimization rule to obtain a second resource node, and deploy the to-be-deployed business to the second resource node.

8. A cloud resource service deployment device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the cloud resource service deployment method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the cloud resource business deployment method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the cloud resource service deployment method according to any one of claims 1 to 6 are implemented.

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