State-Dependent Multi-Constraint Topology Configuration for Software-Defined Service Overlay Networks

Abstract

Service Overlay Network (SON) is an efficient solution for ensuring the end-to-end Quality of Service (QoS) in different real-world applications, including Video-on-Demand, Voice over IP, and other value-added Internet-based services. Although SON offers many advantages, such as ease of deployment and resilience to the node failures, it has to face the challenge of overlay network configuration that needs to dynamically adjust to the change in communication requirements. In this paper, we propose a novel method for adaptive overlay topology configuration, called AOTC based on Software-Defined Networks, deep learning, and reinforcement learning. The intuitive motivation is to address the above challenge, maximize the QoS from two aspects of customer preference and network cost. The obtained experimental results demonstrate the superiority of AOTC. Such a method can significantly reduce network cost while providing an improvement of 50% and 60% in terms of average delay and packet loss rate as compared to other traditional approaches.