NDLI: Web Service Composition Based on Reinforcement Learning

Content Provider	IEEE Xplore Digital Library
Author	Yu Lei Zhou Jiantao Wei Fengqi Gao Yongqiang Yang Bo
Copyright Year	2015
Description	Author affiliation: Inner Mongolia Eng. Lab. of Cloud Comput. & Service Software, Inner Mongolia Univ., Hohhot, China (Yu Lei; Zhou Jiantao; Wei Fengqi; Gao Yongqiang; Yang Bo)
Abstract	How we manage Web services depends on how we understand their variable parts and invariable parts. Studying them separately could make Web service research much easier and make our software architecture much more loose-coupled. We summarize two variable parts that affect Web service compositions: uncertain invocation results and uncertain quality of services. These uncertain factors affect success rate of service composition. Previous studies model the Web service problem as a planning problem, while this problem is considered as an uncertain planning problem in this paper. Specifically, we use Partially Observable Markov Decision Process to deal with the uncertain planning problem for service composition. According to the uncertain model, we propose a reinforcement learning method, which is an uncertainty planning method, to compose web services. The proposed method does not need to know complete information of services, instead it uses historical data and estimates the successful possibilities that services are composed together with respect to service outcomes and QoS. Simulation experiments verify the validity of the algorithm, and the results also show that our method improves the success rate of the service composition.
Starting Page	731
Ending Page	734
File Size	224571
Page Count	4
File Format	PDF
e-ISBN	9781467372725
DOI	10.1109/ICWS.2015.103
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Web services Quality of service Planning Uncertainty Markov processes Learning (artificial intelligence) Computational modeling reinforcement learning algorithm Web service composition optimal policy partially observable markov decision process
Content Type	Text
Resource Type	Article

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