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Open Access

MR-IDPSO: A Novel Algorithm for Large-Scale Dynamic Service Composition

Yanping Zhang, Zihui Jing, Yiwen Zhang( )
School of Computer Science and Technology, Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China.
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Abstract

In the era of big data, data intensive applications have posed new challenges to the field of service composition. How to select the optimal composited service from thousands of functionally equivalent services but different Quality of Service (QoS ) attributes has become a hot research in service computing. As a consequence, in this paper, we propose a novel algorithm MR-IDPSO (MapReduce based on Improved Discrete Particle Swarm Optimization), which makes use of the improved discrete Particle Swarm Optimization (PSO) with the MapReduce to solve large-scale dynamic service composition. Experiments show that our algorithm outperforms the parallel genetic algorithm in terms of solution quality and is efficient for large-scale dynamic service composition. In addition, the experimental results also demonstrate that the performance of MR-IDPSO becomes more better with increasing number of candidate services.

Keywords

MapReduceservice compositionQuality of Service (QoS)parallel particle swarm optimization

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Tsinghua Science and Technology
Volume 20 Issue 6,
December 2015
Pages 602-612
DOI: 10.1109/TST.2015.7349932
Cite this article:
Zhang Y, Jing Z, Zhang Y. MR-IDPSO: A Novel Algorithm for Large-Scale Dynamic Service Composition. Tsinghua Science and Technology, 2015, 20(6): 602-612. https://doi.org/10.1109/TST.2015.7349932

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Received: 17 March 2015
Revised: 01 June 2015
Accepted: 08 June 2015
Published: 17 December 2015
© The author(s) 2015
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