  In this paper, we propose a novel algorithm for energy-efficient, low-latency
dynamic mobile edge computing (MEC), in the context of beyond 5G networks
endowed with Reconfigurable Intelligent Surfaces (RISs). In our setting, new
computing requests are continuously generated by a set of devices and are
handled through a dynamic queueing system. Building on stochastic optimization
tools, we devise a dynamic learning algorithm that jointly optimizes the
allocation of radio resources (i.e., power, transmission rates, sleep mode and
duty cycle), 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 (E2E) 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. The presence and optimization of RISs helps boosting the
performance of dynamic MEC, thanks to the capability to shape and adapt the
wireless propagation environment. Numerical results assess the performance in
terms of service delay, learning, and adaptation capabilities of the proposed
strategy for RIS empowered MEC.
