Hierarchical MapReduce programming model and scheduling algorithms. Luo, Y. & Plale, B. In Proceedings - 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2012, 2012. doi abstract bibtex 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. © 2012 IEEE.
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abstract = {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. © 2012 IEEE.},
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author = {Luo, Y. and Plale, B.},
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