Self-similarity and Multidimensionality: Tools for Performance Modelling of Distributed Infrastructure

  • Authors:
  • Raul Ramirez-Velarde;Cesar Vargas;Gerardo Castañon;Lorena Martinez-Elizalde

  • Affiliations:
  • Tecnologico de Monterrey, Monterrey, Mexico C. P.64849;Tecnologico de Monterrey, Monterrey, Mexico C. P.64849;Tecnologico de Monterrey, Monterrey, Mexico C. P.64849;Tecnologico de Monterrey, Monterrey, Mexico C. P.64849

  • Venue:
  • OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
  • Year:
  • 2008

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Abstract

In this article, we present a model that uses large-deviations and the Pareto probability distribution to model self-similar data in high-performance infrastructure, such as the one found on computational and data grids, transactional and computational clusters, and multimedia streaming. We also show how Principal Component Analysis can reduce dimensionality of data, such as the one produced by different types of problems, user preferences and behaviour, and resource popularity.