In this work, we tackle the energy consumption problem of edge computing technology looking at two key aspects: (i) reducing the energy burden of modern edge computing facilities to the power grid and (ii) distributing the user-generated computing load within the edge while meeting computing deadlines and achieving network level benefits (server load balancing vs consolidation and reduction of transmission costs). In the considered setup, edge servers are co-located with the base stations of a mobile network. Renewable energy sources are available to power base stations and servers, and users generate workload that is to be processed within certain deadlines. We propose a predictive, online and distributed algorithm for the scheduling of computing jobs that attains objectives (i) and (ii). The algorithm achieves fast convergence, leading to an energy efficient use of edge computing facilities, and obtains in the best case a reduction of 50% in the amount of renewable energy that is sold to the power grid by heuristic policies and that is, in turn, used at the network edge for processing.
We study a wireless jamming problem consisting of the competition between a legitimate receiver and a jammer, as a zero-sum game with the value to maximize/minimize being the channel capacity at the receiver's side. Most of the approaches found in the literature consider the two players to be stationary nodes. Instead, we investigate what happens when they can change location, specifically moving along a linear geometry. We frame this at first as a static game, which can be solved in closed form, and subsequently we extend it to a dynamic game, under three different versions for what concerns completeness/perfection of mutual information about the adversary's position, corresponding to different assumptions of concealment/sequentiality of the moves, respectively. We first provide some theoretical conditions that hold for the static game and also help identify good strategies valid under any setup, including dynamic games. Since dynamic games, although more realistic, are characterized by an exploding strategy space, we exploit reinforcement learning to obtain efficient strategies leading to equilibrium outcomes. We show how theoretical findings can be used to train smart agents to play the game, and validate our approach in practical setups.
The energy sustainability of multi-access edge computing (MEC) platforms is here addressed by developing Energy-Aware job Scheduling at the Edge (EASE), a computing resource scheduler for edge servers co-powered by renewable energy resources and the power grid. The scenario under study involves the optimal allocation and migration of time-sensitive computing tasks in a resource-constrained Internet of Vehicles (IoV) context. This is achieved by tackling, as the main objective, the minimization of the carbon footprint of the edge network, whilst delivering adequate quality of service (QoS) to the end users (e.g., meeting task execution deadlines). EASE integrates i) a centralized optimization step, solved through model predictive control (MPC), to manage the renewable energy that is locally collected at the edge servers and their local computing resources, estimating their future availability, and ii) a distributed consensus step, solved via dual ascent in closed form, to reach agreement on service migrations. EASE is compared with four existing migration strategies. Quantitative results demonstrate its greater energy efficiency, which often gets close to complete carbon neutrality, while also improving the QoS.
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