MARVEL: Enabling controller load balancing in software-defined networks with multi-agent reinforcement learning

Abstract

The control plane plays a significant role in Software-Defined Networking (SDN). A large SDN usually implements its control plane with several distributed controllers, each controlling a subset of switches and synchronizing with other controllers to maintain a consistent network view. Under the fluctuating network traffic, a static controller-switch mapping relationship could lead to imbalanced workload allocation. Controllers may getoverloaded and reject new requests, eventually reducing the control plane's request processing ability. Most existing schemes have relied heavily on iterative optimization algorithms to manipulate the mapping relationship between controllers and switches, which are either time-consuming or less satisfactory in terms of performance. In this paper, we propose a dynamic controller workload balancing scheme, that is termed MARVEL, based on multi-agent reinforcement learning for generation of switch migration actions. MARVEL works in two phases: offline training and online decision making. In the training phase, each agent learns how to migrate switches through interacting with the network. In the online phase, MARVEL is deployed to make decisions on migrating switches. Experimental results show that MARVEL outperforms competing existing schemes by improving the control plane's request processing ability at least 27.3% while using 25% less processing time.