Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

Abstract EN

Energy consumption in computer systems has become a more and more important issue.

High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors.

In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors.

Then we propose a particle swarm optimization (PSO) based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions.

Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions.

Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.