The scheme design of switch-controller association is an essential problem for software-defined networking (SDN) systems. A natural idea is to address the problem from the perspective of stable matching, since each switch (controller) often prefers to be associated with those controllers (switches) of lower communication costs and control traffic overhead. However, in practice, such system dynamics are usually unknown a priori, making it a challenging open problem. In this paper, we study such a problem of stable matching between switches and controllers with unknown communication costs from the perspective of multi-agent multi-armed bandit (MAMAB) learning. By integrating stable matching with online learning, we propose an effective Learning- aided Switch-controller Stable Matching (LS2M) scheme. Our theoretical analysis shows that LS2M effectively achieves a switch-optimal stable matching with a sublinear regret bound over time slots. Moreover, we conduct numerical simulations to verify the outperformance of LS2M over various baseline schemes.

Emoji prediction has been widely adopted in most mobile keyboards to improve the quality of user experience. Considering the energy limitations of smartphones, it is promising to consider deploying pre-trained prediction models on edge servers, with which smartphones can carry out emoji prediction in an online fashion. However, given a limited connection capacity, a key issue under such a scheme lies in how each smartphone should select a subset of models to achieve high-accuracy and real-time emoji prediction with energy efficiency (a.k.a. the model selection problem). Moreover, part of the system dynamics such as the accuracy and the latency of individual models are usually unknown a priori in practice, further complicating the problem. In this paper, with an effective integration of history-aware online learning and online control, we propose the first green edge intelligence scheme to solve the model selection problem for edge-assisted mobile keyboard emoji prediction. Our theoretical analysis and simulation results verify the effectiveness of our proposed scheme in achieving a sublinear regret bound and energy efficiency with high accuracy and low latency.

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 regret bound over a finite time horizon.