DeepRoute on Chameleon: Experimenting with Large-scale Reinforcement Learning and SDN on Chameleon Testbed

Abstract

As the numbers of internet users and connected devices continue to multiply, due to big data and Cloud applications, network traffic is growing at an exponential rate. WAN networks, in particular, are witnessing very large traffic spikes cause by large file transfers that last from a few minutes to hours on network links and there is a need to develop innovative ways in which flows can be managed in real-time.In this work, we develop a reinforcement learning approach, in particular Upper-Confidence Algorithm, to learn optimal paths and reroute traffic to improve network utilization. We present throughput and flow diversions using Mininet and demo the technique using Chameleon’s Testbed (Bring-Your-Own-Controller [BYOC] functionality). This work is initial implementation towards DeepRoute, which combines Deep reinforcement learning algorithms with SDN controllers to create and route traffic using deployed OpenFlow switches.