Meaningful statistical analysis of large computational clusters.

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Effective monitoring of large computational clusters demands the analysis of a vast amount of raw data from a large number of machines. The fundamental interactions of the system are not, however, well-defined, making it difficult to draw meaningful conclusions from this data, even if one were able to efficiently handle and process it. In this paper we show that computational clusters, because they are comprised of a large number of identical machines, behave in a statistically meaningful fashion. We therefore can employ normal statistical methods to derive information about individual systems and their environment and to detect problems sooner than ... continued below

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17 p.

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Gentile, Ann C.; Marzouk, Youssef M.; Brandt, James M. & Pebay, Philippe Pierre July 1, 2005.

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Effective monitoring of large computational clusters demands the analysis of a vast amount of raw data from a large number of machines. The fundamental interactions of the system are not, however, well-defined, making it difficult to draw meaningful conclusions from this data, even if one were able to efficiently handle and process it. In this paper we show that computational clusters, because they are comprised of a large number of identical machines, behave in a statistically meaningful fashion. We therefore can employ normal statistical methods to derive information about individual systems and their environment and to detect problems sooner than with traditional mechanisms. We discuss design details necessary to use these methods on a large system in a timely and low-impact fashion.

Physical Description

17 p.

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  • Report No.: SAND2005-4558
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/958384 | External Link
  • Office of Scientific & Technical Information Report Number: 958384
  • Archival Resource Key: ark:/67531/metadc929601

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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Creation Date

  • July 1, 2005

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

Description Last Updated

  • Dec. 6, 2016, 1:02 p.m.

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Gentile, Ann C.; Marzouk, Youssef M.; Brandt, James M. & Pebay, Philippe Pierre. Meaningful statistical analysis of large computational clusters., report, July 1, 2005; United States. (digital.library.unt.edu/ark:/67531/metadc929601/: accessed October 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.