Dynamic Mobile Edge Computing empowered by Reconfigurable Intelligent Surfaces

The goal of this work is to propose a novel algorithm for energy-efficient, low-latency dynamic computation offloading in mobile edge computing (MEC), in the context of 5G (and beyond) networks endowed with Reconfigurable Intelligent Surfaces (RISs). In our setting, new requests for computations are continuously generated at each user, and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic algorithm that jointly optimizes radio resources (i.e., power, rates), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end delay. The proposed strategy is dynamic, since it performs a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown a priori. Numerical results corroborate the benefits of our strategy in the context of RIS-empowered MEC systems.