D2Q Synchronizer: Distributed SDN Synchronization for Time Sensitive Applications

TL;DR

This paper introduces D2Q Synchronizer, a reinforcement learning algorithm for distributed SDN that optimizes network costs and latency for time-sensitive applications across multi-domain networks.

Contribution

It presents a novel RL-based synchronization policy that jointly optimizes network and user performance in distributed SDN environments.

Findings

Reduces network costs by at least 45% compared to heuristics.

Ensures QoS requirements for all user tasks.

Outperforms existing policies in dynamic multi-domain SDN scenarios.

Abstract

In distributed Software-Defined Networking (SDN), distributed SDN controllers require synchronization to maintain a global network state. Despite the availability of synchronization policies for distributed SDN architectures, most policies do not consider joint optimization of network and user performance. In this work, we propose a reinforcement learning-based algorithm called D2Q Synchronizer, to minimize long-term network costs by strategically offloading time-sensitive tasks to cost-effective edge servers while satisfying the latency requirements for all tasks. Evaluation results demonstrate the superiority of our synchronizer compared to heuristic and other learning policies in literature, by reducing network costs by at least 45% and 10%, respectively, while ensuring the QoS requirements for all user tasks across dynamic and multi-domain SDN networks.