Lifespan and energy-oriented load balancing algorithms across sets of nodes in Green Edge Computing

Abstract

Green-powered edge computing architectures allow bringing computation in areas that are not reached by the power grids. More often, in applications for Precision Agriculture and Smart Cities, we could have a set of nodes that are coupled with an accumulator which is, during the day, re-charged by the energy harvested by small solar panels. With the latest advances in technology, the edge node is generally assimilated to be a lowpower Single Board Computer (SBC), and it is able to carry out even relatively demanding tasks. For example, it can run deep learning models to images or video sequences captured in loco by cameras. However, due to the differences in terms of power consumption and weather conditions, each node experiences a different lifespan, some nodes may even shut down prematurely, causing the interruption of the portion of the deployed service. In this paper, we propose three decentralized algorithms that solve the problem by making the nodes cooperatively balance the traffic in order to level and maximize their lifespan. By comparing the approaches in two different experiments by using a cluster of Raspberry Pi 4 we show that our solutions allow to increase the lifespan of the service of 10% on average wrt the case in which no algorithm is applied.