Author ORCID Identifier

https://orcid.org/0000-0002-3912-1470

Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

2.2 ELECTRICAL, ELECTRONIC, INFORMATION ENGINEERING, Electrical and electronic engineering, Communication engineering and systems

Publication Details

Published version on IEEE Xplore

https://ieeexplore.ieee.org/document/9914138

Abstract

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.

DOI

https://doi.org/10.1109/CITDS54976.2022.9914138

Funder

SFI

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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