GA for QoS Satisfaction Degree Optimal Web Service Composition Selection Model As the Web service technology is using more widely in cloud computing, the Web service composition selection has also become one of the most popular research fields. The service composition selection problem is described as an multiobjective optimization problem considering multi QoS parameters and solved by an intelligent optimization algorithm in most of the existing literatures. Feelings of the users are usually ignored. In this paper, the concept of QoS satisfaction degree is introduced and adopted as the optimal objective of the Web service composition selection model. Genetic algorithm (GA) is applied to solve the problem and test result show that the solution prompted in this paper is feasible and effective.

Integrating Reinforcement Learning and Skyline Computing for Adaptive Service Composition Abstract   In service computing, combining multiple services through service composition to address complex user requirements has become a popular research topic. QoSaware 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 largescale 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.