Adaptive Robust Traffic Engineering in Software Defined Networks

TL;DR

This paper presents a novel clustering-based approach for adaptive traffic engineering in SDN that reduces reconfiguration frequency while maintaining near-optimal network performance.

Contribution

It introduces a new clustering method considering routing similarities and overlaps to improve reconfiguration decisions in SDN traffic management.

Findings

Significantly reduces network reconfigurations

Maintains near-optimal routing performance

Outperforms state-of-the-art approaches in simulations

Abstract

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate traffic engineering modules able to optimize network configuration according to traffic. Ideally, network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc. In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in optimal routing and not only in traffic values. Moreover, to provide more flexibility to the online decisions on when applying a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing regardless of the transition instant. We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration.