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

作     者：Yanping Zhang Zihui Jing Yiwen Zhang 

作者机构：School of Computer Science and TechnologyKey Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui University 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2015年第20卷第6期

页      面：602-612页

核心收录：

学科分类：08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 

基　　金：supported by the National Natural Science Foundation of China (No. 61175046) the Natural Science Foundation of Anhui Province of China (No. 1408085MF132) 

主　　题：Map Reduce service composition Quality of Service( 

摘      要：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(Qo S) attributes has become a hot research in service computing. As a consequence,in this paper, we propose a novel algorithm MR-IDPSO(Map Reduce based on Improved Discrete Particle Swarm Optimization), which makes use of the improved discrete Particle Swarm Optimization(PSO) with the Map Reduce 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.