Joint Switch-Controller Association and Control Devolution for SDN Systems: An Integration of Online Control and Online Learning

TL;DR

This paper introduces LASAC, an integrated online learning and control scheme for SDN systems that optimizes switch-controller association and control devolution amidst uncertainties, balancing system costs and queue stability.

Contribution

It formulates a stochastic optimization problem and develops LASAC, a novel scheme combining bandit learning and control techniques for SDN management under uncertainty.

Findings

LASAC achieves a tunable tradeoff between queue stability and cost reduction.

LASAC has a sublinear regret bound over finite time horizons.

Simulation results validate LASAC's effectiveness in dynamic SDN environments.

Abstract

In software-defined networking (SDN) systems, it is a common practice to adopt a multi-controller design and control devolution techniques to improve the performance of the control plane. However, in such systems, the decision-making for joint switch-controller association and control devolution often involves various uncertainties, e.g., the temporal variations of controller accessibility, and computation and communication costs of switches. In practice, statistics of such uncertainties are unattainable and need to be learned in an online fashion, calling for an integrated design of learning and control. In this paper, we formulate a stochastic network optimization problem that aims to minimize time-average system costs and ensure queue stability. By transforming the problem into a combinatorial multi-armed bandit problem with long-term stability constraints, we adopt bandit learning methods and optimal control techniques to handle the exploration-exploitation tradeoff and long-term stability constraints, respectively. Through an integrated design of online learning and online control, we propose an effective Learning-Aided Switch-Controller Association and Control Devolution (LASAC) scheme. Our theoretical analysis and simulation results show that LASAC achieves a tunable tradeoff between queue stability and system cost reduction with a sublinear time-averaged regret bound over a finite time horizon.