Scheduling mix-flow in SD-DCN based on Deep Reinforcement Learning with Private Link

Abstract

In software-defined datacenter networks, there are bandwidth-demanding elephant flows without deadline and delay-sensitive mice flows with strict deadline. They compete with each other for limited network resources, and how to effectively schedule such mix-flow is a huge challenge. We propose DRL-PLink (deep reinforcement learning with private link) that combines software-defined network and deep reinforcement learning (DRL) to schedule mix-flow. It divides the link bandwidth and establishes some corresponding private links for different types of flows respectively to isolate them. DRL is used to adaptively allocate bandwidth resources for these private links. Furthermore, DRL-PLink introduces Clipped Double Q-learning and parameter exploration NoisyNet technology to improve the scheduling policy for overestimated value estimates and action exploration problems in DRL. The simulation results show that DRL-PLink can effectively schedule mix-flow. Compared with ECMP and pFabric, the average flow completion time of DRL-PLink has decreased by 68.87% and 52.18% respectively. At the same time, it maintains a high deadline meet rate (>96.6%) close to pFabric and Karuna very much.