Large scale probabilistic available bandwidth estimation

  • Authors:

  • Frederic Thouin;Mark Coates;Michael Rabbat

  • Affiliations:

  • McGill University, Department of Electrical and Computer Engineering, 3480 University, Montreal, Quebec, Canada H2A 3A7;McGill University, Department of Electrical and Computer Engineering, 3480 University, Montreal, Quebec, Canada H2A 3A7;McGill University, Department of Electrical and Computer Engineering, 3480 University, Montreal, Quebec, Canada H2A 3A7

  • Venue:
  • Computer Networks: The International Journal of Computer and Telecommunications Networking
  • Year:
  • 2011

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Abstract

The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: (i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; (ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; (iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; (iv) almost all tools use ad hoc techniques to address measurement noise; and (v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead than Pathload.