SDN Controller Load Balancing Based on Reinforcement Learning

TL;DR

This paper proposes a reinforcement learning-based load balancing mechanism for SDN controllers that optimizes resource utilization, reduces migration overhead, and improves response times in multi-controller networks.

Contribution

It introduces a novel reinforcement learning approach to achieve global optimal load balancing in SDN controllers with minimal migration costs.

Findings

Balances load efficiently across controllers

Reduces migration overhead significantly

Enhances packet-in response speed

Abstract

Aiming at the local overload of multi-controller deployment in software-defined networks, a load balancing mechanism of SDN controller based on reinforcement learning is designed. The initial paired migrate-out domain and migrate-in domain are obtained by calculating the load ratio deviation between the controllers, a preliminary migration triplet, contains migration domain mentioned above and a group of switches which are subordinated to the migrate-out domain, makes the migration efficiency reach the local optimum. Under the constraint of the best efficiency of migration in the whole and without migration conflict, selecting multiple sets of triples based on reinforcement learning, as the final migration of this round to attain the global optimal controller load balancing with minimum cost. The experimental results illustrate that the mechanism can make full use of the controllers' resources, quickly balance the load between controllers, reduce unnecessary migration overhead and get a faster response rate of the packet-in request.