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

作者：Zhang Yanping; Jing Zihui; Zhang Yiwen^*

来源：Tsinghua Science and Technology, 2015, 20(6): 602-612.

DOI：10.1109/tst.2015.7349932

• MapReduce
• service composition
• Quality of Service (QoS)
• parallel particle swarm optimization

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.

• 出版日期2015-12
• 单位安徽大学