Deep Reinforcement Learning for Energy-efficient Selection of Embedded Services at the Edge

Edge computing helps to release the tension at the center of IoT systems’ networks, thus reducing the latency, optimizing the bandwidth, and providing new privacy and security solutions, among others. Despite its benefits, edge computing faces unique challenges, including latency, security, and resource constraints. Among these challenges, energy consumption has emerged in the research community, and the global objective is to do "more with less". Researchers explore diverse strategies to enhance sustainability, from hardware optimizations to intelligent algorithms. The quest for energy efficiency and to reduce several impacts aligns with broader efforts to create an environmentally conscious technology landscape.In this paper, we present a task selection model for the edge. We focus on energy consumption and aim to maximize the value given by tasks, all the while minimizing the energy consumed. To do so, we develop a computer environment to simulate outdoor energy-harvesting edge devices, contribute to research reproducibility by recreating a photovoltaic energy harvesting prediction model, and train deep reinforcement learning models to select the best set of tasks at the edge. Our best deep reinforcement learning model, which uses Trust Region Policy Optimization, outperforms our best heuristic and is a robust task selector under varying external conditions.