@inproceedings{
 title = {Hierarchical MapReduce programming model and scheduling algorithms},
 type = {inproceedings},
 year = {2012},
 id = {f1d717a8-d6b3-3a0f-9eaf-2350f163faee},
 created = {2019-10-01T17:20:47.239Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2019-10-01T17:23:31.719Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Luo2012},
 folder_uuids = {73f994b4-a3be-4035-a6dd-3802077ce863},
 private_publication = {false},
 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.},
 bibtype = {inproceedings},
 author = {Luo, Y. and Plale, B.},
 doi = {10.1109/CCGrid.2012.132},
 booktitle = {Proceedings - 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2012}
}