{"title":"Extending Scientific Workflow Systems to Support MapReduce Based Applications in the Cloud","authors":"Shashank Gugnani, T. Kiss","doi":"10.1109/IWSG.2015.15","DOIUrl":null,"url":null,"abstract":"Cloud Computing has gained a lot of popularity in recent years because of the flexibility that it offers. In addition, there seems to be a rising interest in combining Parallel Computing, Cloud Computing and Big Data to create large scale scientific applications. WS-PGRADE is a gateway framework that allows users to create such applications by defining them as scientific workflows. This paper investigates how workflow systems and science gateways, such as WS-PGRADE, can be extended with data processing capabilities of Hadoop based on the MapReduce paradigm in the cloud. Analysis shows the methods described to integrate Hadoop with workflows and science gateways work well in different scenarios and can be used to create massively parallel applications for scientific analysis of Big Data.","PeriodicalId":341012,"journal":{"name":"2015 7th International Workshop on Science Gateways","volume":"21 8","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 7th International Workshop on Science Gateways","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWSG.2015.15","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Cloud Computing has gained a lot of popularity in recent years because of the flexibility that it offers. In addition, there seems to be a rising interest in combining Parallel Computing, Cloud Computing and Big Data to create large scale scientific applications. WS-PGRADE is a gateway framework that allows users to create such applications by defining them as scientific workflows. This paper investigates how workflow systems and science gateways, such as WS-PGRADE, can be extended with data processing capabilities of Hadoop based on the MapReduce paradigm in the cloud. Analysis shows the methods described to integrate Hadoop with workflows and science gateways work well in different scenarios and can be used to create massively parallel applications for scientific analysis of Big Data.
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