Journal
INFORMATION SCIENCES
Volume 519, Issue -, Pages 141-160Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.01.039
Keywords
Service composition; QoS; Reinforcement learning; Skyline computing; Adaptability
Categories
Funding
- National Key Research and Development Program [2018YFB1003800]
- NSFC [61672152, 61232007, 61532013]
- Collaborative Innovation Center of Novel Software Technology and Industrialization
- Collaborative Innovation Center of Wireless Communications Technology
- NSF IIS [IIS-1814450]
- ONR [N00014-18-1-2875]
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In service computing, combining multiple services through service composition to address complex user requirements has become a popular research topic. QoS-aware service composition aims to find the optimal composition scheme with the QoS attributes that best match user requirements. However, certain QoS attributes may continuously change in a dynamic service environment, so service composition methods need to be adaptive. Furthermore, the large number of candidate services poses a key challenge for service composition, where existing service composition approaches based on reinforcement learning (RL) suffer from low efficiency. To deal with the problems above, in this paper, a new service composition approach is proposed which combines RL with skyline computing where the latter is used for reducing the search space and computational complexity. A WSCMDP model is proposed to solve the large-scale service composition within a dynamically changing environment. To verify the proposed method, a series of comparative experiments are conducted, and the experimental results demonstrate the effectiveness, scalability and adaptability of the proposed approach. (C) 2020 Elsevier Inc. All rights reserved.
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