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Hierarchical MapReduce Programming Model and Scheduling Algorithms

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2 Author(s)
Yuan Luo ; Sch. of Inf. & Comput., Indiana Univ. Bloomington, Bloomington, IN, USA ; Plale, B.

We present a Hierarchical MapReduce framework that gathers computation resources from different clusters and runs MapReduce jobs across them. The applications implemented in this framework adopt the Map-Reduce-Global Reduce model where computations are expressed as three functions: Map, Reduce, and Global Reduce. Two scheduling algorithms are introduced: Compute Capacity Aware Scheduling for compute-intensive jobs and Data Location Aware Scheduling for data-intensive jobs. Experimental evaluations using a molecule binding prediction tool, Auto Dock, and grep demonstrate promising results for our framework.

Published in:

Cluster, Cloud and Grid Computing (CCGrid), 2012 12th IEEE/ACM International Symposium on

Date of Conference:

13-16 May 2012

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