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dc.contributor.supervisor Thulasiraman, Parimala (Computer Science) Thulasiram, Ruppa (Computer Science) en_US
dc.contributor.author Sidhu, Manitpal S.
dc.date.accessioned 2013-06-27T21:24:05Z
dc.date.available 2013-06-27T21:24:05Z
dc.date.issued 2013-06-27
dc.identifier.uri http://hdl.handle.net/1993/21692
dc.description.abstract The idea of utilizing nature inspired algorithms to find near optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is the task matching problem in large heterogeneous distributed computing environments like Grids and Clouds. Researchers have explored Particle Swarm Optimization(PSO), which is branch of swarm intelligence, to find a near optimal solution for the task matching problem. In this work, I investigated the effectiveness of the smallest position value (SPV) technique in mapping the continuous version of the PSO algorithm to the task matching problem in a heterogeneous computing environment. The experimental evaluation demonstrated that the task matching generated by this technique will result in an imbalanced load distribution. In this work, I have therefore also designed a load-rebalance PSO heuristic (PSO-LR) that results in minimization of makespan and balanced utilization of the available compute nodes even in heterogeneous computing environments. en_US
dc.subject PSO en_US
dc.subject Scheduling en_US
dc.title A PSO based load-rebalance algorithm for task-matching in large scale heterogeneous computing systems en_US
dc.degree.discipline Computer Science en_US
dc.contributor.examiningcommittee Graham, Peter (Computer Science) Appadoo, S.S. (Supply Chain Management) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2013 en_US


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