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Integrating reinforcement learning and skyline computing for adaptive service composition.
- Source :
-
Information Sciences . May2020, Vol. 519, p141-160. 20p. - Publication Year :
- 2020
-
Abstract
- 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 WSC-MDP 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *REINFORCEMENT learning
*ADAPTIVE computing systems
*COMPUTATIONAL complexity
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 519
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 142002666
- Full Text :
- https://doi.org/10.1016/j.ins.2020.01.039