Abstract
Web service composition is one of the core technologies of realizing service-oriented computing. Web service composition satisfies the requirements of users to form new value-added services by composing existing services. As Cloud Computing develops, the emergence of Web services with different quality yet similar functionality has brought new challenges to service composition optimization problem. How to solve large-scale service composition in the Cloud Computing environment has become an urgent problem. To tackle this issue, this paper proposes a parallel optimization approach based on Spark distributed environment. Firstly, the parallel covering algorithm is used to cluster the Web services. Next, the multiple clustering centers obtained are used as the starting point of the particles to improve the diversity of the initial population. Then, according to the parallel data coding rules of resilient distributed dataset (RDD), the large-scale combination service is generated with the proposed algorithm named Spark Particle Swarm Optimization Algorithm (SPSO). Finally, the usage of particle elite selection strategy removes the inert particles to optimize the performance of the combination of service selection. This paper adopts real data set WS-Dream to prove the validity of the proposed method with a large number of experimental results.
Highlights
IntroductionMore and more users publish their resources in the form of Web services to promote the use of service
As big data develops, more and more users publish their resources in the form of Web services to promote the use of service
This paper, uses the parallel covering algorithm [21] to cluster multiple candidate services based on their Quality of Service (QoS) properties and ensure that the population particles are randomly distributed in these initial starting points
Summary
More and more users publish their resources in the form of Web services to promote the use of service. We propose a novel large-scale service selection method based on distributed computing environment, Spark [8], using the parallel particle swarm method to solve the service composition problem. (1) Based on the service selection characteristics of big service, we propose SPSO service selection method This method uses the combined potentials of Spark, Security and Communication Networks covering algorithm and particle swarm algorithm. Spark is used for parallelization, covering algorithm for reducing the initial search space, and particle swarm algorithm for optimization of service selection These three techniques are combined to solve the problem of large-scale service selection. The rest of this paper is organized as follows: Section 2 introduces related work; Section 3 presents Web service composition model; Section 4 introduces the improved particle swarm method; Section 5 verifies the effectiveness of our approaches through simulating experiments.
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