Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

Performance Evaluation of Local Area Network based on Support Vector Machine

Authors
Qi Liu, Yin Liu, Yiyong Lin, Ling He, Yunzhi Huang
Corresponding Author
Qi Liu
Available Online March 2016.
DOI
https://doi.org/10.2991/icmmct-16.2016.384How to use a DOI?
Keywords
IP performance evaluation; support vector machine; k-fold cross validation; local area network
Abstract
This work presents an IP performance evaluation method, based on the Support Vector Machine (SVM). In this work, eight network parameters are collected: CPU utilization of switchboard, utilization of memory, network link-off, delay, delay jitter, bandwidth, bit rate of transmission and bit rate of reception. The performance of the local area network is classified into three grades: excellent, good and failed. In this work, the collected network parameters are processed using SVM classifier. The average classification accuracy is achieved using k-fold cross validation method. The experiment results indicate that the classification accuracy of the proposed method is over 90%. This proposed evaluation system could be applied to a real-time network performance evaluation application effectively.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
Series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmct-16.2016.384How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Qi Liu
AU  - Yin Liu
AU  - Yiyong Lin
AU  - Ling He
AU  - Yunzhi Huang
PY  - 2016/03
DA  - 2016/03
TI  - Performance Evaluation of Local Area Network based on Support Vector Machine
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 1927
EP  - 1930
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.384
DO  - https://doi.org/10.2991/icmmct-16.2016.384
ID  - Liu2016/03
ER  -