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

Published: 13 April 2015 Publication 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.
[2]
Smola, A. J., and Schölkopf, B. A tutorial on support vector regression, Statistics and Computing, 14, 3, 2004, 199--222.
[3]
http://traffic.comics.unina.it/Traces/hobbit.php
[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.
[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.

Cited By

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  • (2024)Leveraging Graph Neural Networks for SLA Violation Prediction in Cloud ComputingIEEE Transactions on Network and Service Management10.1109/TNSM.2023.329239221:1(605-620)Online publication date: Feb-2024
  • (2019)Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a DatasetIEEE Access10.1109/ACCESS.2019.29237067(82649-82671)Online publication date: 2019
  • (2017)Hierarchical support vector regression for QoS prediction of network traffic dataProceedings of the 1st International Conference on Internet of Things and Machine Learning10.1145/3109761.3158386(1-6)Online publication date: 17-Oct-2017

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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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    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
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 13 April 2015

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    Author Tags

    1. QoS
    2. SVM
    3. SVR
    4. jitter
    5. latency
    6. network traffic
    7. throughput

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    SAC 2015
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    SAC 2015: Symposium on Applied Computing
    April 13 - 17, 2015
    Salamanca, Spain

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    SAC '15 Paper Acceptance Rate 291 of 1,211 submissions, 24%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    Cited By

    View all
    • (2024)Leveraging Graph Neural Networks for SLA Violation Prediction in Cloud ComputingIEEE Transactions on Network and Service Management10.1109/TNSM.2023.329239221:1(605-620)Online publication date: Feb-2024
    • (2019)Analysing Cloud QoS Prediction Approaches and Its Control Parameters: Considering Overall Accuracy and Freshness of a DatasetIEEE Access10.1109/ACCESS.2019.29237067(82649-82671)Online publication date: 2019
    • (2017)Hierarchical support vector regression for QoS prediction of network traffic dataProceedings of the 1st International Conference on Internet of Things and Machine Learning10.1145/3109761.3158386(1-6)Online publication date: 17-Oct-2017

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