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 article, 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.

中文翻译：

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SDN 系统的联合交换机控制器关联和控制委托：控制和学习的集成在线视角


在软件定义网络（SDN）系统中，通常采用多控制器设计和控制委托技术来提高控制平面的性能。然而，在此类系统中，联合开关-控制器关联和控制委托的决策通常涉及各种不确定性，例如控制器可访问性的时间变化以及开关的计算和通信成本。在实践中，这种不确定性的统计是无法实现的，需要通过在线方式学习，需要学习和控制的一体化设计。在本文中，我们提出了一个随机网络优化问题，旨在最小化时间平均系统成本并确保队列稳定性。通过将问题转化为具有长期稳定性约束的组合多臂老虎机问题，我们采用老虎机学习方法和最优控制技术分别处理探索-利用权衡和长期稳定性约束。通过在线学习和在线控制的集成设计，我们提出了一种有效的学习辅助开关控制器关联和控制委托（LASAC）方案。我们的理论分析和模拟结果表明，LASAC 在有限时间范围内实现了队列稳定性和系统成本降低之间的可调权衡，并具有亚线性时间平均后悔界限。