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QoS prediction for network data traffic using hierarchical modified regularized least squares rough support vector regression

Published:13 April 2015Publication History

ABSTRACT

In this paper, we present a novel approach for predicting QoS on networks having complex traffic and multitenant architecture. We propose a soft computing based hierarchical modified regularized least squares rough support vector regression approach on network traffic to deliver better QoS. QoS prediction takes advantage of past service usage experiences. It does not consume any additional invocations of network services and avoids time consuming real world service incantations. We discuss the proposed approach and provide important aspects of QoS prediction. The experiments are conducted on real world public dataset and compared with benchmark dataset. The results show that proposed approach achieves high prediction accuracies than other techniques.

References

  1. Wang, L., Ranjan, R., Chen, J., and Benatallah, B. Cloud Computing: Methodology, Systems and Applications, CRC Press, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Smola, A. J., and Schölkopf, B. A tutorial on support vector regression, Statistics and Computing, 14, 3, 2004, 199--222. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. http://traffic.comics.unina.it/Traces/hobbit.phpGoogle ScholarGoogle Scholar
  4. Welch, L. R., and Shirszi, B. A. A dynamic real time benchmark for assessment of QoS and resource management technology. In Proceedings of 5th IEEE Real Time Technology and Applications Symposium, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Chaudhuri, A. 2013. Hierarchical modified regularized least squares fuzzy support vector regression through multiscale approach. In Advances in Computational Intelligence, I. Rojas, G. Joya and J. Cabestany, Editors. Lecture Notes in Computer Science, Springer Verlag, 393--407. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. QoS prediction for network data traffic using hierarchical modified regularized least squares rough support vector regression

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    • Published in

      cover image ACM Conferences
      SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing
      April 2015
      2418 pages
      ISBN:9781450331968
      DOI:10.1145/2695664

      Copyright © 2015 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 April 2015

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      Acceptance Rates

      SAC '15 Paper Acceptance Rate291of1,211submissions,24%Overall Acceptance Rate1,650of6,669submissions,25%

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