MiFi: Bounded Update to Optimize Network Performance in Software-Defined Data Centers

Abstract

A controller needs to solve the multi-commodity flow problem and globally update the network under tight time constraints to maintain optimal network configurations. This centralized optimization in data centers involves many variables and constraints, which has a slow convergence speed and little scalability. In this paper, we propose MiFi, which aims to Minimize Flow cost or intuitively average transmission delay (delay or latency of flows), 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 mostly affected in terms of latency and configuration in the next update. Then, we set a limitation range (the subset of switches requiring network updates) for updating them to improve adaptability and scalability by updating a less number of flows each time to achieve fast operations. Further, based on the Drift-Plus-Penalty method in Lyapunov theory, we propose a heuristic policy without prior information of flow demand and a renewal policy with a performance guarantee to minimize the additive optimality gap. To the best of our knowledge, MiFi is the first paper that studies the range and frequency of flow reconfigurations, which has both theoretical and practical significance in the area. Emulations and numerical simulations, which are much better than the estimated theoretical bound, show that MiFi outperforms the state of the art algorithms in terms of latency by over 45% while making improvements in adaptability and scalability.