{"title":"Resource management in data-intensive clouds: Opportunities and challenges","authors":"David E. Irwin, P. Shenoy, E. Cecchet, M. Zink","doi":"10.1109/LANMAN.2010.5507156","DOIUrl":null,"url":null,"abstract":"Today's cloud computing platforms have seen much success in running compute-bound applications with time-varying or one-time needs. In this position paper, we will argue that the cloud paradigm is also well suited for handling data-intensive applications, characterized by the processing and storage of data produced by high-bandwidth sensors or streaming applications. The data rates and the processing demands vary over time for many such applications, making the on-demand cloud paradigm a good match for their needs. However, today's cloud platforms need to evolve to meet the storage, communication, and processing demands of data-intensive applications. We present an ongoing GENI project to connect high-bandwidth radar sensor networks with computational and storage resources in the cloud and use this example to highlight the opportunities and challenges in designing end-to-end data-intensive cloud systems.","PeriodicalId":201451,"journal":{"name":"2010 17th IEEE Workshop on Local & Metropolitan Area Networks (LANMAN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 17th IEEE Workshop on Local & Metropolitan Area Networks (LANMAN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/LANMAN.2010.5507156","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
Today's cloud computing platforms have seen much success in running compute-bound applications with time-varying or one-time needs. In this position paper, we will argue that the cloud paradigm is also well suited for handling data-intensive applications, characterized by the processing and storage of data produced by high-bandwidth sensors or streaming applications. The data rates and the processing demands vary over time for many such applications, making the on-demand cloud paradigm a good match for their needs. However, today's cloud platforms need to evolve to meet the storage, communication, and processing demands of data-intensive applications. We present an ongoing GENI project to connect high-bandwidth radar sensor networks with computational and storage resources in the cloud and use this example to highlight the opportunities and challenges in designing end-to-end data-intensive cloud systems.