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

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Title

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

Creator

  • Author: Li, Jie
    Creator Type: Personal
  • Author: Humphrey, Marty
    Creator Type: Personal
  • Author: Cheah, You-Wei
    Creator Type: Personal
  • Author: Ryu, Youngryel
    Creator Type: Personal
  • Author: Agarwal, Deb
    Creator Type: Personal
  • Author: Jackson, Keith
    Creator Type: Personal
  • Author: Ingen, Catharine van
    Creator Type: Personal

Contributor

  • Sponsor: Lawrence Berkeley National Laboratory. Computational Research Division.
    Contributor Type: Organization

Publisher

  • Name: Lawrence Berkeley National Laboratory
    Place of Publication: Berkeley, California
    Additional Info: Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA (United States)

Date

  • Creation: 2010-04-01

Language

  • English

Description

  • Content Description: 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.
  • Physical Description: 9

Subject

  • Keyword: Escience
  • Keyword: Redundancy
  • Keyword: Clouds
  • Keyword: Data Processing
  • Keyword: Cloud Computing
  • Keyword: Programming
  • Keyword: Windows Cloud Computing
  • Keyword: Windows Azure
  • Keyword: Design
  • STI Subject Categories: 97 Mathematical Methods And Computing
  • Keyword: Satellites
  • Keyword: Performance
  • Keyword: Tolerance

Source

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

Collection

  • Name: Office of Scientific & Technical Information Technical Reports
    Code: OSTI

Institution

  • Name: UNT Libraries Government Documents Department
    Code: UNTGD

Resource Type

  • Article

Format

  • Text

Identifier

  • 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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