DocumentCode :
2733027
Title :
Scheduling dynamic load-balancing in parallel and distributed computers using modified genetic algorithm with time dependent fitness function
Author :
Mohammadzadeh, Javad ; Moeinzadeh, M-Hossein ; Sharifian-R, Sarah ; Mahdavi, Leila
Author_Institution :
Comput. Dept., Islamic Azad Univ. of Karaj Branch, Karaj, Iran
Volume :
1
fYear :
2009
fDate :
20-22 Nov. 2009
Firstpage :
894
Lastpage :
898
Abstract :
Load Balancing has many applications in various systems, but specifically plays a major role in the efficiency of parallel and distributed systems. In these systems, by load balancing we mean scheduling the jobs in a way that every job could be executed concurrently while it is mapped to a processing unit, such as a processor (in a multi-processor system) or a computer (in a grid computer). By developing effective methods the whole program time execution will be decreased and process utilization will be optimized. In this paper, a solution is proposed for dynamic load balancing. Because of the NP-hard nature of the problem, heuristic methods are desired. A simple scheduling method, Round Robin, and Genetic algorithm are discussed as previous methods for this problem and in order to improve the results a new modification of Genetic Algorithm is presented.
Keywords :
computational complexity; genetic algorithms; parallel processing; resource allocation; scheduling; NP-hard problem; distributed computers; genetic algorithm; load balancing; parallel computers; process utilization; round robin; scheduling; time dependent fitness function; Application software; Concurrent computing; Distributed computing; Dynamic scheduling; Genetic algorithms; Grid computing; Load management; Optimization methods; Processor scheduling; Round robin;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
Type :
conf
DOI :
10.1109/ICICISYS.2009.5358051
Filename :
5358051
Link To Document :
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