Delay Minimization for Edge Computing with Dynamic Server Computing Capacity: A Learning Approach

摘要

The offloading decisions of $K$ mobile users (MUs) aiming at minimizing the execution delay in a Mobile Edge Computing (MEC) scenario with non-orthogonal multiple access is considered. In this work, we assume a time-varying MEC server computing capacity which exploits additional computing resources that are freed over time, but are not known beforehand. In this setting, the optimal offloading decision depends on the different tasks of MUs, their channel fading processes and the MEC server computing capacity. We first formulate the optimization problem and identify two main challenges, namely, how to exploit the given incomplete knowledge to minimize the delay and how to handle the high dimensionality of the problem. To address these challenges, we propose a novel reinforcement learning (RL) algorithm, termed combinatorial offloading learning (COL). The name stands for its ability to handle the combinatorial nature of the solutions. Exploiting the available knowledge, we learn the offloading decision policy aiming at minimizing the delay. Furthermore, we handle the curse of dimensionality, typical of combinatorial problems, by splitting the learning task, solving $K+1$ smaller RL problems and using linear function approximation. Through numerical simulations, we show that COL could perform similar to a short term optimal solution with complete information and exhaustive search, and outperforms known strategies like the greedy approach.