Energy Efficient Real-Time Tasks Scheduling on High-Performance Edge-Computing Systems Using Genetic Algorithm

Abstract

With an increase in the number of processing cores or systems, the high-performance edge-computing system’s power consumption along with its computational speed will increase, essentially. However, this comes at the expense of high-energy utilization. One notable solution to reduce the energy consumption of these systems 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 scheduling approach that associates the genetic algorithm (GA) with the first feasible speed (FiFeS) technique i.e. GA-FiFeS algorithm. This does not jeopardize real-time tasks’ deadlines. The GA-FiFeS algorithm proposes an energy-efficient schedule while still ensuring high response times. The results of the proposed approach, using plausible assumptions and experimental parameters, are compared with currently in-practice approaches, i.e. FiFeS and LeFeS (least feasible speed) approaches. Using numerical simulations and plausible assumptions, our investigation suggests that the proposed GA-FiFeS technique outperforms the FiFeS technique in terms of energy consumption (~18.56%) and response times (~2.78%). Furthermore, the GA-FiFeS has comparable outcomes with the LeFeS method while taking the expected time of execution as an assessment feature for analysis.

Keywords

• Genetic algorithm
• edge-computing
• multi-core
• real-time systems
• feasibility analysis
• HPC