Gil Einziger

Deploying artificial intelligence models at the network edge has emerged as a promising alternative to cloud-based inference. However, real-world performance is often limited by unreliable connectivity, fluctuating resource availability, and the complexity of scheduling tasks across heterogeneous devices. To address these challenges, we formulate edge inference scheduling as a sequential decision-making problem and develop an adaptive scheduling algorithm based on a contextual bandit framework. Specifically, we employ a linear upper confidence bound (UCB) contextual bandit to learn network dynamics and guide the scheduling of incoming inference tasks. The performance superiority of the proposed method over other scheduling strategies in terms of inference latency is illustrated through large-scale simulations and further verified by real-world experiments on a physical testbed across a wide range of operating conditions.