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In software-defined networking (SDN) systems, it is a common practice toadopt a multi-controller design and control devolution techniques to improvethe performance of the control plane. However, in such systems, thedecision-making for joint switch-controller association and control devolutionoften involves various uncertainties, e.g., the temporal variations ofcontroller accessibility, and computation and communication costs of switches.In practice, statistics of such uncertainties are unattainable and need to belearned in an online fashion, calling for an integrated design of learning andcontrol. In this paper, we formulate a stochastic network optimization problemthat aims to minimize time-average system costs and ensure queue stability. Bytransforming the problem into a combinatorial multi-armed bandit problem withlong-term stability constraints, we adopt bandit learning methods and optimalcontrol techniques to handle the exploration-exploitation tradeoff andlong-term stability constraints, respectively. Through an integrated design ofonline learning and online control, we propose an effective Learning-AidedSwitch-Controller Association and Control Devolution (LASAC) scheme. Ourtheoretical analysis and simulation results show that LASAC achieves a tunabletradeoff between queue stability and system cost reduction with a sublineartime-averaged regret bound over a finite time horizon.

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