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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.


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

Show Author's information Yanping ZhangZihui JingYiwen Zhang( )
School of Computer Science and Technology, Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China.

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:

MapReduce, service composition, Quality of Service (QoS), parallel particle swarm optimization
Received: 17 March 2015 Revised: 01 June 2015 Accepted: 08 June 2015 Published: 17 December 2015 Issue date: December 2015
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Publication history
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Publication history

Received: 17 March 2015
Revised: 01 June 2015
Accepted: 08 June 2015
Published: 17 December 2015
Issue date: December 2015

Copyright

© The author(s) 2015

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61175046), the Natural Science Foundation of Anhui Province of China (No. 1408085MF132).

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