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Article

Machine-Learned Recognition of Network Traffic for Optimization through Protocol Selection

Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
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Author to whom correspondence should be addressed.
Academic Editor: Paolo Bellavista
Received: 1 May 2021 / Revised: 1 June 2021 / Accepted: 4 June 2021 / Published: 11 June 2021
We introduce optimization through protocol selection (OPS) as a technique to improve bulk-data transfer on shared wide-area networks (WANs). Instead of just fine-tuning the parameters of a network protocol, our empirical results show that the selection of the protocol itself can result in up to four times higher throughput in some key cases. However, OPS for the foreground traffic (e.g., TCP CUBIC, TCP BBR, UDT) depends on knowledge about the network protocols used by the background traffic (i.e., other users). Therefore, we build and empirically evaluate several machine-learned (ML) classifiers, trained on local round-trip time (RTT) time-series data gathered using active probing, to recognize the mix of network protocols in the background with an accuracy of up to 0.96. View Full-Text
Keywords: network probing; machine-learned classifier; protocol selection; wide-area networks; bandwidth sharing; data transfer; shared network; fairness network probing; machine-learned classifier; protocol selection; wide-area networks; bandwidth sharing; data transfer; shared network; fairness
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MDPI and ACS Style

Anvari, H.; Lu, P. Machine-Learned Recognition of Network Traffic for Optimization through Protocol Selection. Computers 2021, 10, 76. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060076

AMA Style

Anvari H, Lu P. Machine-Learned Recognition of Network Traffic for Optimization through Protocol Selection. Computers. 2021; 10(6):76. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060076

Chicago/Turabian Style

Anvari, Hamidreza, and Paul Lu. 2021. "Machine-Learned Recognition of Network Traffic for Optimization through Protocol Selection" Computers 10, no. 6: 76. https://0-doi-org.brum.beds.ac.uk/10.3390/computers10060076

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