Fault Tolerance and Scaling in e-Science Cloud Applications: Observations from the Continuing Development of MODISAzure

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It can be natural to believe that many of the traditional issues of scale have been eliminated or at least greatly reduced via cloud computing. That is, if one can create a seemingly wellfunctioning cloud application that operates correctly on small or moderate-sized problems, then the very nature of cloud programming abstractions means that the same application will run as well on potentially significantly larger problems. In this paper, we present our experiences taking MODISAzure, our satellite data processing system built on the Windows Azure cloud computing platform, from the proof-of-concept stage to a point of being able to run ... continued below

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Li, Jie; Humphrey, Marty; Cheah, You-Wei; Ryu, Youngryel; Agarwal, Deb; Jackson, Keith et al. April 1, 2010.

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It can be natural to believe that many of the traditional issues of scale have been eliminated or at least greatly reduced via cloud computing. That is, if one can create a seemingly wellfunctioning cloud application that operates correctly on small or moderate-sized problems, then the very nature of cloud programming abstractions means that the same application will run as well on potentially significantly larger problems. In this paper, we present our experiences taking MODISAzure, our satellite data processing system built on the Windows Azure cloud computing platform, from the proof-of-concept stage to a point of being able to run on significantly larger problem sizes (e.g., from national-scale data sizes to global-scale data sizes). To our knowledge, this is the longest-running eScience application on the nascent Windows Azure platform. We found that while many infrastructure-level issues were thankfully masked from us by the cloud infrastructure, it was valuable to design additional redundancy and fault-tolerance capabilities such as transparent idempotent task retry and logging to support debugging of user code encountering unanticipated data issues. Further, we found that using a commercial cloud means anticipating inconsistent performance and black-box behavior of virtualized compute instances, as well as leveraging changing platform capabilities over time. We believe that the experiences presented in this paper can help future eScience cloud application developers on Windows Azure and other commercial cloud providers.

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  • 24th IEEE International Parallel and Distributed Precessing Symposium, IPDPS, Atlanta, Georgia, April, 2010

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  • Report No.: LBNL-5060E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 1026809
  • Archival Resource Key: ark:/67531/metadc837439

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  • April 1, 2010

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Li, Jie; Humphrey, Marty; Cheah, You-Wei; Ryu, Youngryel; Agarwal, Deb; Jackson, Keith et al. Fault Tolerance and Scaling in e-Science Cloud Applications: Observations from the Continuing Development of MODISAzure, article, April 1, 2010; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc837439/: accessed October 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.