End-to-end available bandwidth as a random autocorrelated QoS-relevant time-series

  • Authors:
  • Alexander Chobanyan;Matt Mutka;Vidiadhar Mandrekar;Ning Xi

  • Affiliations:
  • Logistics/Demand Planning, Nestle USA Food Company, 800 N Brand Blvd, Glendale, 91203, CA, United States;Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, United States;Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, United States;Department of Electrical Engineering, Michigan State University, East Lansing, MI 48824, United States

  • Venue:
  • Computer Networks: The International Journal of Computer and Telecommunications Networking
  • Year:
  • 2008

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Abstract

Estimating the reliability of an end-to-end network path is critically important for applications that support remote real-time task execution. Available bandwidth defined as a minimum spare capacity of links constituting a network path is an important QoS characteristic of the path. In this work we demonstrate a new approach to modeling available bandwidth behavior from a time-series analysis prospective. In particular, we introduce a notion of crossing probability - the probability that the available bandwidth drops below the QoS critical threshold for the period of time required for the real-time task execution. We estimate the ''crossing probability'' by an application of the ARCH^2 (AutoRegressive Conditional Heteroscedasticity) model to the available bandwidth behavior. We characterize the network path by model coefficients @b"0 and @b"1, which may be evaluated and updated dynamically. We use these coefficients to quickly output the ''crossing probability'' for arbitrary values of the threshold and length of the real-time task. The model was evaluated on real bandwidth measurements across multiple network paths.