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On the Design of Adaptive and Decentralized Load Balancing Algorithms with Load Estimation for Computational Grid Environments
December 2007 (vol. 18 no. 12)
pp. 1675-1686
In this paper, we address several issues that are imperative to Grid environments such as, handling resource heterogeneity and sharing, communication latency, job migration from one site to other, and load balancing.We address these issues by proposing two job migration algorithms, which are MELISA (Modified ELISA) and LBA (Load Balancing on Arrival). The algorithms differ in the way load balancing is carried out and is shown to be efficient in minimizing the response time on large and small scale heterogeneous Grid environments, respectively. MELISA, applicable to large scale systems (i.e., interGrid [1]), is a modified version of ELISA [2] in which we consider job migration cost, resource heterogeneity and network heterogeneity when load balancing is considered. LBA algorithm, applicable for small scale systems (i.e., intraGrid [1]), performs load balancing by estimating expected finish time of job on buddy processors on each job arrival. Both algorithms estimate system parameters such as job arrival rate, CPU processing rate, load at processor and balance the load by migrating jobs to buddy processors by taking into account job transfer cost, resource heterogeneity and network heterogeneity. We quantify the performance of our algorithms using several influencing parameters such as, job size, data transfer rate, status exchange period, migration limit, and we discuss the implications of the performance and choice of our approaches.

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Index Terms:
Grid systems, load balancing, average response time, communication delays, migration
Citation:
Ruchir Shah, Bhardwaj Veeravalli, Manoj Misra, "On the Design of Adaptive and Decentralized Load Balancing Algorithms with Load Estimation for Computational Grid Environments," IEEE Transactions on Parallel and Distributed Systems, vol. 18, no. 12, pp. 1675-1686, Dec. 2007, doi:10.1109/TPDS.2007.1115
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