Adaptive Routing Reconfigurations to Minimize Flow Cost in SDN-Based Data Center Networks

Abstract

Data center networks have become heavily reliant on software-defined network to orchestrate data transmission. To maintain optimal network configurations, a controller needs to solve the multi-commodity flow problem and globally update the network under tight time constraints. In this paper, we aim to minimize flow cost or intuitively average transmission delay, under reconfiguration budget constraints in data centers. Thus, we formulate this optimization problem as a constrained Markov Decision Process and propose a set of algorithms to solve it in a scalable manner. We first develop a propagation algorithm to identify the flows which are mostly affected in terms of latency and will be configured in the next network update. Then, we set a limitation range for updating them to improve adaptability and scalability by updating a less number of flows each time to achieve fast operations as well. Further, based on the Drift-Plus-Penalty method in Lyapunov theory, we propose a heuristic policy without prior information of flow demand with a performance guarantee to minimize the additive optimality gap. To the best of our knowledge, this is the first paper that studies the range and frequency of flow reconfigurations, which has both theoretical and practical significance in the related area. Extensive emulations and numerical simulations, which are much better than the estimated theoretical bound, show that our proposed policy outperform the state of the art algorithms in terms of latency by over 45% while making improvements in adaptability and scalability.