Open Access   Article

Mapreduce- A Fabric Clustered Approach to Equilibrate the Load

Deepti Sharma1 , Vijay B. Aggarwal2

Section:Research Paper, Product Type: Journal Paper
Volume-4 , Issue-3 , Page no. 116-123, Mar-2016

Online published on Mar 30, 2016

Copyright © Deepti Sharma , Vijay B. Aggarwal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Deepti Sharma , Vijay B. Aggarwal, “Mapreduce- A Fabric Clustered Approach to Equilibrate the Load”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.116-123, 2016.

MLA Style Citation: Deepti Sharma , Vijay B. Aggarwal "Mapreduce- A Fabric Clustered Approach to Equilibrate the Load." International Journal of Computer Sciences and Engineering 4.3 (2016): 116-123.

APA Style Citation: Deepti Sharma , Vijay B. Aggarwal, (2016). Mapreduce- A Fabric Clustered Approach to Equilibrate the Load. International Journal of Computer Sciences and Engineering, 4(3), 116-123.

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In recent years, load balancing is the challenging task which affects the performance in allotting the resources on homogeneous and heterogeneous cluster computing environment. This research proposes an enhancement in ACCS (Adaptively Circulates job among all servers by taking account of both Client activity and System load) policies by incorporating Map Reduce to overcome the problem in balancing the workload for resources. This technique provides simplicity and flexibility for data partitioning, localization and processing jobs as indicated by their present sizes and ranks the servers based on their loads by giving high priority to the smaller jobs. Map Reduce emphasizes more on high throughput of data on low-latency of job execution in a cluster to accomplish huge execution advantages. Trace driven simulations demonstrate the viability and robustness of Map Reduce under numerous different situations.

Key-Words / Index Term

Load Balancing, Map Reduce, Web Server Clusters, AdaptLoad, ACCS


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