Author ORCID Identifier
Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence
2.2 ELECTRICAL, ELECTRONIC, INFORMATION ENGINEERING, Electrical and electronic engineering, Communication engineering and systems
Traffic classification is a crucial aspect for Software-Defined Networking functionalities. This paper is a part of an on-going project aiming at optimizing power consumption in the environment of software-defined datacenter networks. We have developed a novel routing strategy that can blindly balance between the power consumption and the quality of service for the incoming traffic flows. In this paper, we demonstrate how to classify the network traffic flows so that the quality of service of each flow-class can be guaranteed efficiently. This is achieved by creating a dataset that encompasses different types of network traffic such as video, VoIP, game and ICMP. The performance of a number of Machine Learning techniques is compared and the results are reported. As part of future work, we will incorporate classification into the power consumption model towards achieving further advances in this research area.
M. Nsaif, G. Kovásznai, M. Abboosh, A. Malik and R. d. Fréin, "ML-Based Online Traffic Classification for SDNs," 2022 IEEE 2nd Conference on Information Technology and Data Science (CITDS), Debrecen, Hungary, 2022, pp. 217-222, doi: 10.1109/CITDS54976.2022.9914138.