Multi-constraint service selection method and device based on global QoS decomposition

Abstract

The invention relates to a multi-constraint service selection method and device based on global QoS decomposition, which is realized by establishing a single object optimization model with a number of constraints. The method comprises the steps that a corresponding dependency set and collision set are established for each candidate service according to the service dependency transmission characteristic; the global QoS constraint is decomposed into local QoS constraints corresponding to each service class; candidate services which do not satisfy the local QoS constraints under the service class are filtered; all filtered candidate services are checked, and the dependency set and collision set of the remaining candidate services are updated; an adaptive replacement method is used to carry out quality scale combination replacement in an unresolved state; the local fitness of each candidate service is calculated; and the candidate service with the greatest local adaptability in each service class is selected to form the final combination service. According to the invention, the complexity and running time are greatly optimized; the real-time demand of a user is satisfied; the scale of a candidate service space is narrowed; and the quality and performance of the network combination service are effectively ensured.

Description

technical field [0001] The invention belongs to the technical field of computer networks, and in particular relates to a multi-constraint service selection method based on global QoS decomposition and a device thereof. Background technique [0002] As a new type of distributed computing model, Service Oriented Computing (Service Oriented Computing, referred to as SOC) can seamlessly combine various Web services distributed in heterogeneous networks dynamically to form large-grained combined Web services. services to meet the increasingly complex needs of users. With the prosperity and development of the Web market, there are a large number of Web services on the network with overlapping functional attributes. ) made a request. However, due to various uncertain factors in the network environment, some services may experience functional failure or QoS mutation during operation. Great significance. At present, a large amount of research work is focused on service selection ...

AI Technical Summary

Problems solved by technology

However, due to various uncertain factors in the network environment, some services may experience functional failure or QoS mutation during operation. great significance

At present, a large amount of research work is focused on service selection under global QoS constraints. Existing QoS-based service selection methods have the following shortcomings: (1) Usually, in user requirements for service combinations, users only End-to-end QoS constraints, without local constraints for each basic service class

At present, there are a large number of solutions based on global QoS constraints, using intelligent evolutionary algorithms to optimally search composite services, but these algorithms generally have relatively high computational complexity, and the running time of the algorithms is heavily dependent on the size of the candidate service set

Once the number of Web services in the network increases, the running time will increase exponentially, and it will be difficult to meet the real-time requirements of users

(2) The existing service selection methods only consider the global QoS constraints proposed by users, but ignore the possible functional constraints between candidate services

Some schemes introduce a hill-climbing repair operator in the genetic algorithm, which can repair the schemes in the population that do not satisfy the conflict dependency constraints between services, thereby guiding the optimization direction of the algorithm, but when the scale of constraints between services increases, the repair time Will grow exponentially, so it is difficult to meet the real-time needs of users

Part of the scheme proposes a multi-constraint service selection method based on local approximate filtering, which quickly filters the candidate service space through global QoS constraints and inter-service functional constraints, and then uses the directed particle swarm optimization algorithm to search for the best candidate service among the remaining candidate services. However, this method only uses an approximate filtering strategy when decomposing the global QoS, so that the final composite service solution may not be able to meet the user's global QoS constraints

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