Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark
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 qual...
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| Veröffentlicht in: | Security and communication networks 2017-01, Vol.2017 (2017), p.1-8 |
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| container_title | Security and communication networks |
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| creator | Li, Wei Zhang, Yiwen Chen, Shanshan Guo, Xing |
| description | 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. |
| doi_str_mv | 10.1155/2017/9097616 |
| format | Article |
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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.</description><identifier>ISSN: 1939-0114</identifier><identifier>EISSN: 1939-0122</identifier><identifier>DOI: 10.1155/2017/9097616</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Cloud computing ; Clustering ; Composition ; Distributed processing ; Efficiency ; Experiments ; Genetic algorithms ; Internet service providers ; Manufacturing ; Optimization algorithms ; Particle swarm optimization ; Population ; Principal components analysis ; Quality of service ; User requirements ; User satisfaction ; Web services</subject><ispartof>Security and communication networks, 2017-01, Vol.2017 (2017), p.1-8</ispartof><rights>Copyright © 2017 Xing Guo et al.</rights><rights>Copyright © 2017 Xing Guo et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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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). 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| source | Wiley Online Library; EZB Electronic Journals Library |
| subjects | Algorithms Cloud computing Clustering Composition Distributed processing Efficiency Experiments Genetic algorithms Internet service providers Manufacturing Optimization algorithms Particle swarm optimization Population Principal components analysis Quality of service User requirements User satisfaction Web services |
| title | Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on Spark |
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