Energy Efficient Real-time Tasks Scheduling on High Performance Edge- Computing Systems using Genetic Algorithm

Abstract

With increase in number of processing cores or systems, the high-performance edge-computing system’s power and computation speed will increase essentially. However, this comes at the expense of high-energy utilization. One notable solution to reduce the energy consumption of these system is to execute these systems at the slowest feasible speed so that the job’s deadline times are met. Unfortunately, this method is at the expense of more response time and performance loss. To resolve this issue, in this paper we propose a GA-FiFeS approach that associates genetic algorithm with the first feasible speed (FiFeS) approach. This does not jeopardize real-time task deadlines. The GA-FiFeS proposes an energy-efficient schedule while still ensuring high response times. The results of the proposed approach, using plausible assumptions, are compared with currently in practice approaches i.e. FiFeS and LeFeS. As far as energy usage and response times are concerned, the GA-FiFeSS approach surpasses the FiFeS and LeFeS strategies, respectively.