Paper Info
Title
A Fine-Grained Video Traffic Control Mechanism in Software-Defined Networks
Abstract
We investigate how to provide Quality-of-Service (QoS) for diversified video flows. We design a fine-grained video traffic control mechanism that integrates traffic classification with path selection for video flows within the framework of SDN. For the design, we present a category-theoretic ontology log (olog) diagram model, which provides a novel perspective on the interdependency among various system components. For the video traffic classification, we first evaluate various machine learning classifiers in terms of their performance and then chose the most effective one to be the first module. For the path selection, we devise a multi-constrained QoS routing strategy by restructuring a state-of-the-art graph algorithm, combine it with the k-shortest path algorithm, and deploy this strategy as another video traffic control module. We implemented a prototype of the proposed mechanism on the SDN emulator Mininet, and we evaluate its effectiveness using the performance results obtained.
Year
DOI
Venue
2022
10.1109/TNSM.2022.3164335
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
Keywords
DocType
Volume
Software-defined networks (SDN), traffic classification, quality-of-service (QoS), path selection
Journal
19
Issue
ISSN
Citations 
3
1932-4537
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Jun Huang100.34
Qiang Duan232737.37
Cong-Cong Xing300.34
Bo Gu4808.11
Guodong Wang500.34
Sherali Zeadally63399219.68
Erich Baker700.34