A fast genetic algorithm based static heuristic for scheduling independent tasks on heterogeneous systems

Menghani, Gaurav

doi:10.1109/pdgc.2010.5679877

Cited by 2 publications

(1 citation statement)

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“…Although it is an NP-hard problem, there are many approximation algorithms for solving the problem of realtime task allocation in a heterogeneous processor environment, including traditional real-time task scheduling algorithms such as deadline-monotonic (DM) algorithm [4], rate-monotonic (RM) algorithm [5], least-laxity-first (LLF) algorithm [6], earliest-deadline-first (EDF) algorithm [5], and linear programming-based (LP) algorithm [7] and the swarm intelligence algorithms such as ant colony optimization (ACO) [8], genetic algorithm (GA) [9][10][11], and shuffled frog-leaping algorithm (SFLA) [12,13]. In these studies, most algorithms do not consider energy consumption factors.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Xie

Cao

et al. 2014

Mathematical Problems in Engineering

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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.

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