DocumentCode :
2467796
Title :
An Efficient Genetic Algorithm for Task Scheduling in Heterogeneous Distributed Computing Systems
Author :
Daoud, Mohammad I. ; Kharma, Nawwaf
Author_Institution :
Concordia Univ., Montreal
fYear :
0
fDate :
0-0 0
Firstpage :
3258
Lastpage :
3265
Abstract :
Task scheduling plays an important role in the operation of distributed computing systems. Because of its importance, several task scheduling algorithms are proposed in the literature, mainly for homogeneous processors. Few scheduling algorithms are proposed for heterogeneous distributed computing systems (HeDCSs). In this paper, we present a new approach which uses a customized genetic algorithm to produce high-quality tasks schedules for HeDCSs. The performance of the new algorithm is compared to that of two leading scheduling algorithms for HeDCSs. The comparison, which is based on both randomly generated task graphs and task graphs of certain real-world numerical applications, exhibits the supremacy of the new algorithm over the older ones, in terms of schedule length, speedup and efficiency.
Keywords :
distributed processing; genetic algorithms; graph theory; scheduling; task analysis; genetic algorithm; heterogeneous distributed computing systems; randomly generated task graph; task scheduling; Application software; Computer industry; Distributed computing; Dynamic scheduling; Evolutionary computation; Genetic algorithms; High-speed networks; Job shop scheduling; Processor scheduling; Scheduling algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9487-9
Type :
conf
DOI :
10.1109/CEC.2006.1688723
Filename :
1688723
Link To Document :
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