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.
@inproceedings{
title = {Hierarchical MapReduce programming model and scheduling algorithms},
type = {inproceedings},
year = {2012},
id = {48db13c5-26bd-3e44-b0b7-38469121f18e},
created = {2018-03-05T18:20:21.155Z},
file_attached = {false},
profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
group_id = {9d761a94-2f2d-31ce-a8c3-50aa6d668643},
last_modified = {2018-03-05T18:20:21.155Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {false},
hidden = {false},
citation_key = {Luo2012},
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}
}
Downloads: 0
{"_id":"NbaxT3P6TdZrswT6P","bibbaseid":"luo-plale-hierarchicalmapreduceprogrammingmodelandschedulingalgorithms-2012","downloads":0,"creationDate":"2018-03-12T19:10:27.476Z","title":"Hierarchical MapReduce programming model and scheduling algorithms","author_short":["Luo, Y.","Plale, B."],"year":2012,"bibtype":"inproceedings","biburl":"https://bibbase.org/service/mendeley/42d295c0-0737-38d6-8b43-508cab6ea85d","bibdata":{"title":"Hierarchical MapReduce programming model and scheduling algorithms","type":"inproceedings","year":"2012","id":"48db13c5-26bd-3e44-b0b7-38469121f18e","created":"2018-03-05T18:20:21.155Z","file_attached":false,"profile_id":"42d295c0-0737-38d6-8b43-508cab6ea85d","group_id":"9d761a94-2f2d-31ce-a8c3-50aa6d668643","last_modified":"2018-03-05T18:20:21.155Z","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"citation_key":"Luo2012","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","bibtex":"@inproceedings{\n title = {Hierarchical MapReduce programming model and scheduling algorithms},\n type = {inproceedings},\n year = {2012},\n id = {48db13c5-26bd-3e44-b0b7-38469121f18e},\n created = {2018-03-05T18:20:21.155Z},\n file_attached = {false},\n profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},\n group_id = {9d761a94-2f2d-31ce-a8c3-50aa6d668643},\n last_modified = {2018-03-05T18:20:21.155Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n citation_key = {Luo2012},\n private_publication = {false},\n 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.},\n bibtype = {inproceedings},\n author = {Luo, Y. and Plale, B.},\n doi = {10.1109/CCGrid.2012.132},\n booktitle = {Proceedings - 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGrid 2012}\n}","author_short":["Luo, Y.","Plale, B."],"biburl":"https://bibbase.org/service/mendeley/42d295c0-0737-38d6-8b43-508cab6ea85d","bibbaseid":"luo-plale-hierarchicalmapreduceprogrammingmodelandschedulingalgorithms-2012","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":0},"search_terms":["hierarchical","mapreduce","programming","model","scheduling","algorithms","luo","plale"],"keywords":[],"authorIDs":[],"dataSources":["zgahneP4uAjKbudrQ","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}