1 D-MEC: Discontinuous Mobile Edge Computing Mattia Merluzzi, Student Member, IEEE, Nicola di Pietro, Paolo Di Lorenzo, Senior Member, IEEE, Emilio Calvanese Strinati, Member, IEEE, Sergio Barbarossa, Fellow, IEEE Abstract We propose a novel strategy for energy-efficient dynamic computation offloading, in the context of edge-computing-aided 5G (and beyond) networks. The goal is to minimize the energy consumption of the overall system, comprising users and network elements, 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 access point and the edge server, 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 Discontinuous Mobile Edge Computing (D-MEC): an online algorithm that dynamically and optimally orchestrates the sleep mode operations and the duty cycles of the network’s elements. In such a dynamic 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. D-MEC hinges on stochastic Lyapunov 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 users. Several numerical results illustrate the advantages of the proposed method. Index Terms Edge Computing, 5G and Beyond, Green Networking, Computation Offloading, Energy Efficiency. M. Merluzzi, P. Di Lorenzo and S. Barbarossa are with the Department of Information Engineering, Electronics, and Telecommunications of Sapienza University, via Eudossiana 18, 00184 Roma, Italy. E-mail: mattia.merluzzi@uniroma1.it, paolo.dilorenzo@uniroma1.it, sergio.barbarossa@uniroma1.it N. di Pietro and E. Calvanese Strinati are with CEA-LETI, Grenoble, France. Email: nicola.dipietro@cea.fr, emilio.calvanese- strinati@cea.fr. This work was supported by the H2020 EU/Taiwan Project 5G CONNI, Nr. 861459, by the CPS4EU project, which has received funding from the ECSEL Joint Undertaking (JU) under grant agreement Nr. 826276, by MIUR under the PRIN Liquid Edge contract, and by Sapienza University of Rome “Bandi di Ateneo per la ricerca 2019”. arXiv:2008.03508v1 [eess.SP] 8 Aug 2020