<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</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%">Jong Youl Choi</style></author><author><style face="normal" font="default" size="100%">Seung-Hee Bae</style></author><author><style face="normal" font="default" size="100%">Judy Qiu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cloud Computing Paradigms for Pleasingly Parallel Biomedical Applications</style></title><secondary-title><style face="normal" font="default" size="100%">Concurrency and Computation: Practice and Experience</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/27/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://grids.ucs.indiana.edu/ptliupages/publications/ecmls_jour_15.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">John Wiley and Sons</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Cloud computing offers exciting new approaches for scientific computing that leverages the hardware and software investments on large scale data centers by major commercial players. Loosely coupled problems are very important in many scientific fields and are on the rise with the ongoing move towards data intensive computing. There exist several approaches to leverage clouds &amp; cloud oriented data processing frameworks to perform pleasingly parallel computations. In this paper we present three pleasingly parallel biomedical applications, 1) assembly of genome fragments 2) sequence alignment and similarity search 3) dimension reduction in the analysis of chemical structures, implemented utilizing cloud infrastructure service based utility computing models of Amazon Web Services and Microsoft Windows Azure as well as utilizing MapReduce based data processing frameworks, Apache Hadoop and Microsoft DryadLINQ. We review and compare each of the frameworks and perform a comparative study among them based on performance, efficiency, cost and the usability. Cloud service based utility computing model and the managed parallelism (MapReduce) exhibited comparable performance and efficiencies for the applications we considered. We analyze the variations in cost between the different platform choices (eg: EC2 instance types), highlighting the need to select the appropriate platform based on the nature of the computation.</style></abstract><orig-pub><style face="normal" font="default" size="100%">Selected papers from Emerging Computational Methods for the Life Sciences Workshop (ECMLS) of ACM HPDC 2010 conference</style></orig-pub></record></records></xml>