International Journal of Computer Theory and Engineering

Abstract

Systems as asymmetric multiprocessor platforms are considered power-efficient multiprocessor architectures, efficient task partitioning (assignment) and play a crucial role in achieving more energy efficiency at these multiprocessor platforms. This paper addresses the problem of energy-aware static partitioning of periodic real time tasks on heterogeneous multiprocessor platforms. A modified Particle Swarm Optimization variant based on min-min and priority assignment algorithms for task partitioning is proposed. The proposed approach aims to minimize the overall energy consumption, meanwhile avoid deadline violations. An energy-aware cost function is proposed to be considered in the proposed approach. Extensive simulated experiments and comparisons with related approaches have been conducted and the achieved results demonstrate that the proposed partitioning scheme significantly outperforms in terms of the number of executed iterations to accomplish a specific task in addition to the energy savings.