4.7 Article

A Reliable and Efficient Distributed Service Composition Approach in Pervasive Environments

期刊

IEEE TRANSACTIONS ON MOBILE COMPUTING
卷 16, 期 5, 页码 1231-1245

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TMC.2016.2591544

关键词

Mobile service composition; multi-objective optimization; function graph decomposition; sub-solution optimization; combinatorial optimization

资金

  1. China National Science Foundation [61272438, 61472253]
  2. Research Funds of Science and Technology Commission of Shanghai Municipality [15411952502, 14511107702]
  3. Cross Research Fund of Biomedical Engineering of Shanghai Jiaotong University [YG2015MS61]

向作者/读者索取更多资源

The global, ubiquitous usage of smart handsets and diversewireless communication tools calls for ameticulous reexamination of complex and dynamic service componentization and remote invocation. In order to satisfy ever-increasing service requirements and enrich users' experiences, efficient service composition approaches, which leverage the computing resources on nearby devices to form an on-demand composite service, should be developed. This is especially true for situations that are confronted with limited local computing capacity and device mobility. For anymobile pervasive environment, execution reliability and latency of the composite service aremajor concerns that impact users' satisfaction. In this paper, we propose a novel three-staged approach which takes reliability and latency into account to solve a distributed service composition efficiently. First, the graph of the functional process description is decomposed into multiple path structures through a graph-traversing algorithm. Second, messages are forwarded among the network nodes (i.e., intelligent handsets) to search for the sub-solutions for these path structures. Finally, an efficient combinatorial optimization algorithm computes the optimal service composition by the selection from these sub-solutions. This approach is validated extensively in static and mobile environments, and the results show the effectiveness and outperformance of this approach over existing approaches.

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