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Storage and Analysis

SCHEDULE: NOV 12-18, 2011

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Purlieus: Locality-aware Resource Allocation for MapReduce in a Cloud

SESSION: MapReduce


TIME: 11:00AM - 11:30AM

AUTHOR(S):Balaji Palanisamy, Aameek Singh, Ling Liu, Bhushan Jain


We present Purlieus, a MapReduce cloud resource allocation system aimed at enhancing the performance of MapReduce jobs in the cloud. Purlieus provisions virtual MapReduce clusters in a locality-aware manner enabling MapReduce virtual machines (VMs) access to input data and importantly, intermediate data from local or close-by physical machines. We demonstrate how this locality-awareness during both map and reduce phases of the job not only improves runtime performance of individual jobs but also has an additional advantage of reducing network traffic generated in the cloud data center. This is accomplished using a novel coupling of, otherwise independent, data and VM placement steps. We conduct a detailed evaluation of Purlieus and demonstrate significant savings in network traffic and almost 50% reduction in job execution times for a variety of workloads.

Chair/Author Details:

Balaji Palanisamy - Georgia Institute of Technology

Aameek Singh - IBM Research - Almaden

Ling Liu - Georgia Institute of Technology

Bhushan Jain - IBM India Software Lab

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The full paper can be found in the ACM Digital Library

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