Paper Info
Title
Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark.
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.
Year
DOI
Venue
2017
10.1155/2017/9097616
SECURITY AND COMMUNICATION NETWORKS
Field
DocType
Volume
Particle swarm optimization,Population,Spark (mathematics),Distributed Computing Environment,Computer science,Computer network,Web service,Cluster analysis,Optimization problem,Distributed computing,Cloud computing
Journal
2017
ISSN
Citations 
PageRank 
1939-0114
2
0.37
References 
Authors
14
4
Name
Order
Citations
PageRank
Xing Guo174.52
Shanshan Chen288.03
Yiwen Zhang3285.81
Wei Li4436140.67