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.

在服务计算中，通过服务组合来组合多个服务以解决复杂的用户需求已成为流行的研究主题。QoS感知服务组合旨在找到具有最匹配用户需求的QoS属性的最佳组合方案。但是，某些QoS属性在动态服务环境中可能会不断变化，因此服务组合方法需要具有自适应性。此外，大量候选服务对服务组合提出了关键挑战，其中基于增强学习（RL）的现有服务组合方法效率低下。为了解决上述问题，本文提出了一种新的服务组合方法，将RL与天际线计算相结合，其中天际线用于减少搜索空间和计算复杂度。提出了一种WSC-MDP模型来解决动态变化环境中的大规模服务组合。为了验证该方法的有效性，进行了一系列的对比实验，实验结果证明了该方法的有效性，可扩展性和适应性。