  In this work, we study the problem of energy-efficient computation offloading
enabled by edge computing. In the considered scenario, multiple users
simultaneously compete for limited radio and edge computing resources to get
offloaded tasks processed under a delay constraint, with the possibility of
exploiting low power sleep modes at all network nodes. The radio resource
allocation takes into account inter- and intra-cell interference, and the duty
cycles of the radio and computing equipment have to be jointly optimized to
minimize the overall energy consumption. To address this issue, we formulate
the underlying problem as a dynamic long-term optimization. Then, based on
Lyapunov stochastic optimization tools, we decouple the formulated problem into
a CPU scheduling problem and a radio resource allocation problem to be solved
in a per-slot basis. Whereas the first one can be optimally and efficiently
solved using a fast iterative algorithm, the second one is solved using
distributed multi-agent reinforcement learning due to its non-convexity and
NP-hardness. The resulting framework achieves up to 96.5% performance of the
optimal strategy based on exhaustive search, while drastically reducing
complexity. The proposed solution also allows to increase the network's energy
efficiency compared to a benchmark heuristic approach.
