<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T. Gunarathne</style></author><author><style face="normal" font="default" size="100%">Tak-Lon Wu</style></author><author><style face="normal" font="default" size="100%">Judy Qiu</style></author><author><style face="normal" font="default" size="100%">Geoffrey C. Fox</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MapReduce in the Clouds for Science</style></title><secondary-title><style face="normal" font="default" size="100%">2nd IEEE International Conference on Cloud Computing Technology and Science (CloudCom2010)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Proceedings of the 2nd IEEE International Conference on Cloud Computing Technology and Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">AzureMapReduce</style></keyword><keyword><style  face="normal" font="default" size="100%">Cloud Computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Elastic MapReduce</style></keyword><keyword><style  face="normal" font="default" size="100%">Hadoop</style></keyword><keyword><style  face="normal" font="default" size="100%">MapReduce</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://grids.ucs.indiana.edu/ptliupages/publications/CloudCom2010-MapReduceintheClouds.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Indianapolis, IN</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Utility computing model introduced by cloud computing together with the rich set of cloud infrastructure services offers a very viable alternative for the traditional servers and compute clusters. MapReduce distributed data processing architecture has become the weapon of choice for data intensive analyses in the clouds and in commodity clusters due to its fault tolerance features, scalability and the ease of use. Currently there are several options for using MapReduce in the cloud environments such as using MapReduce as a service, setting up your own MapReduce cluster on cloud instances as well as using specialized cloud MapReduce runtimes which take advantage of the cloud infrastructure services. In this paper we evaluate the use and performance of MapReduce in the cloud environments for scientific applications using DNA sequence assembly and sequence alignment as use cases. We also introduce and evaluate the concept of AzureMapReduce, a novel MapReduce runtime build using the Microsoft Azure cloud infrastructure services.</style></abstract></record></records></xml>