Enhancing latency performance through intelligent bandwidth allocation decisions: a survey and comparative study of machine learning techniques

Ruan, Lihua, Dias, Maluge P. I. and Wong, Elaine 2020, Enhancing latency performance through intelligent bandwidth allocation decisions: a survey and comparative study of machine learning techniques, Journal of Optical Communications and Networking, vol. 12, no. 4, pp. B20-B32, doi: 10.1364/jocn.379715.

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Title Enhancing latency performance through intelligent bandwidth allocation decisions: a survey and comparative study of machine learning techniques
Author(s) Ruan, Lihua
Dias, Maluge P. I.ORCID iD for Dias, Maluge P. I. orcid.org/0000-0003-0773-8166
Wong, Elaine
Journal name Journal of Optical Communications and Networking
Volume number 12
Issue number 4
Start page B20
End page B32
Total pages 13
Publisher The Optical Society
Place of publication Washington, D.C.
Publication date 2020-04
ISSN 1943-0620
1943-0639
Summary Converged access networks consolidating 5G and beyond and fixed optical fiber access are expected to support future latency-sensitive human-to-machine applications over the Tactile Internet. Making intelligent bandwidth allocation decisions among end users/machines/robots of the converged network is thus crucial to meeting stringent latency requirements. The recent renewed interest in machine learning (ML) has contributed towards a plethora of undeniable performance improvements in communication networks. Current insights into how ML can be exploited to provide intelligent bandwidth allocation decisions to enhance latency performance, along with guidance on the most suitable ML technique in that regard, remain elusive. This paper provides the first insights, to the best of our knowledge, into the suitability of commonly adopted ML techniques for this purpose by first presenting an in-depth survey focusing on the technical details of these techniques and how each technique is used in existing studies. The benefits, drawbacks, resultant time and space complexity incurred, and prediction accuracy are then evaluated for each ML technique reviewed. Next, a comprehensive comparative study of the ML techniques is presented for the first time, to our best knowledge, to provide guidance on the selection of ML technique that provides intelligent bandwidth allocation decisions towards supporting emerging latency-sensitive applications. The uplink latency performance of a converged network adopting an artificial neural network (ANN) supervised bandwidth allocation scheme is then compared with those arising from using existing bandwidth allocation schemes. Results highlight the ability of the ANN to learn the association among bandwidth demand, network parameters, and the resulting uplink latency such that, in operation, the allocated bandwidth will always be optimized to enhance latency performance.
Language eng
DOI 10.1364/jocn.379715
Indigenous content off
Field of Research 0906 Electrical and Electronic Engineering
0915 Interdisciplinary Engineering
1005 Communications Technologies
HERDC Research category C1.1 Refereed article in a scholarly journal
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145006

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