Swarm Intelligence Approaches for Grid Load Balancing |
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Authors: | Simone A Ludwig Azin Moallem |
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Affiliation: | 1. Department of Computer Science, North Dakota State University, Fargo, ND, USA
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Abstract: | With the rapid growth of data and computational needs, distributed systems and computational Grids are gaining more and more
attention. The huge amount of computations a Grid can fulfill in a specific amount of time cannot be performed by the best
supercomputers. However, Grid performance can still be improved by making sure all the resources available in the Grid are
utilized optimally using a good load balancing algorithm. This research proposes two new distributed swarm intelligence inspired
load balancing algorithms. One algorithm is based on ant colony optimization and the other algorithm is based on particle
swarm optimization. A simulation of the proposed approaches using a Grid simulation toolkit (GridSim) is conducted. The performance
of the algorithms are evaluated using performance criteria such as makespan and load balancing level. A comparison of our
proposed approaches with a classical approach called State Broadcast Algorithm and two random approaches is provided. Experimental
results show the proposed algorithms perform very well in a Grid environment. Especially the application of particle swarm
optimization, can yield better performance results in many scenarios than the ant colony approach. |
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Keywords: | |
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