A framework for QoS-aware traffic classification using semi-supervised machine learning in SDNs

No Thumbnail Available
Authors
Wang, Pu
Lin, Shih-Chun
Luo, Min
Advisors
Issue Date
2016
Type
Conference paper
Keywords
Traffic classification , SDN , QoS , Semi-supervised machine learning
Research Projects
Organizational Units
Journal Issue
Citation
P. Wang, S. C. Lin and M. Luo, "A Framework for QoS-aware Traffic Classification Using Semi-supervised Machine Learning in SDNs," 2016 IEEE International Conference on Services Computing (SCC), San Francisco, CA, 2016, pp. 760-765
Abstract

In this paper, a QoS-aware traffic classification framework for software defined networks is proposed. Instead of identifying specific applications in most of the previous work of traffic classification, our approach classifies the network traffic into different classes according to the QoS requirements, which provide the crucial information to enable the fine-grained and QoS-aware traffic engineering. The proposed framework is fully located in the network controller so that the real-time, adaptive, and accurate traffic classification can be realized by exploiting the superior computation capacity, the global visibility, and the inherent programmability of the network controller. More specifically, the proposed framework jointly exploits deep packet inspection (DPI) and semi-supervised machine learning so that accurate traffic classification can be realized, while requiring minimal communications between the network controller and the SDN switches. Based on the real Internet data set, the simulation results show the proposed classification framework can provide good performance in terms of classification accuracy and communication costs.

Table of Contents
Description
Click on the URL to access the article (may not be free).
Publisher
IEEE
Journal
Book Title
Series
2016 IEEE International Conference on Services Computing (SCC);
PubMed ID
DOI
ISSN
EISSN