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.

Reinforcement LearningDriven QoSAware Intelligent Routing for SoftwareDefined Networks Softwaredefined network (SDN) is an emerging computer networking technology that disjoints the data forwarding from the centralized control and enables a highly manageable and flexible networking paradigm. There has been intensive research developed for efficient routing and resource allocation for SDNs. However, there still remain essential challenges to achieve situationawareness networking management to ensure the applicationdriven QualityofService (QoS) even in the presence of cyber attacks. To address this issue, in this paper, we exploit reinforcement learning (RL) technologies to develop a situationawareness and intelligent networking management from the perspective of routing management. The performance of our proposed RLenabled routing management method is evaluated in the simulation sections by considering various scenarios.