  We propose a novel strategy for energy-efficient dynamic computation
offloading, in the context of edge-computing-aided beyond 5G networks. The goal
is to minimize the energy consumption of the overall system, comprising
multiple User Equipment (UE), an access point (AP), and an edge server (ES),
under constraints on the end-to-end service delay and the packet error rate
performance over the wireless interface. To reduce the energy consumption, we
exploit low-power sleep operation modes for the users, the AP and the ES,
shifting the edge computing paradigm from an always on to an always available
architecture, capable of guaranteeing an on-demand target service quality with
the minimum energy consumption. To this aim, we propose an online algorithm for
dynamic and optimal orchestration of radio and computational resources called
Discontinuous Computation Offloading (DisCO). In such a framework, end-to-end
delay constraints translate into constraints on overall queueing delays,
including both the communication and the computation phases of the offloading
service. DisCO hinges on Lyapunov stochastic optimization, does not require any
prior knowledge on the statistics of the offloading traffic or the radio
channels, and satisfies the long-term performance constraints imposed by the
users. Several numerical results illustrate the advantages of the proposed
method.

Signal Processing