Bandwidth variability prediction with rolling interval least squares (RILS)

  • Authors:
  • Pavan R. Marupally;Vamsi Paruchuri;Chenyi Hu

  • Affiliations:
  • University of Central Arkansas;University of Central Arkansas;University of Central Arkansas

  • Venue:
  • Proceedings of the 50th Annual Southeast Regional Conference
  • Year:
  • 2012

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Abstract

Real Time prediction of end-to-end bandwidth is crucial for network quality of service. It remains a challenge for providing prediction in good quality. This is mainly because of uncertainties involved in network communication. Very recently a new algorithm called Rolling Interval Least Squares (RILS) [3] has been developed which significantly improved the variability forecast of the stock market. In this paper we extend the interval computing approach and applied RILS to innovatively predict bandwidth based on a wide range of rates. Experimental results indicate that RILS improved the quality of bandwidth variability prediction by about 24% when compared to current methods.