Machine-Learned Classifiers for Protocol Selection on a Shared Network

Anvari, Hamidreza; Huard, Jesse; Lu, Paul

doi:10.1007/978-3-030-19945-6_7

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Scope: Tcp Schemes Cubic and Bbr1

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2020

2023

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Cited by 4 publications

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“…For the purpose of building our model, we only consider TCP-based background traffic, under the simplified assumption that CUBIC and BBR are the only protocols available. According to our previous study [11][12][13], the interaction between CUBIC and BBR schemes could be characterized as in Figure 10a.…”

Section: Scope: Tcp Schemes Cubic and Bbrmentioning

confidence: 99%

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

Anvari

2021

Computers

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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.

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Section: Scope: Tcp Schemes Cubic and Bbrmentioning

confidence: 99%