Machine-learnt versus analytical models of TCP throughput
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Using active measurement tools for computing model features provides predictions as accurate as relying on ideal passive measurements.
“Moreover, the evaluation show that using active measurement tools for computing the model features, provide predictions as accurate as relying on ideal passive measurements.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [238]
The SVR model yields more predictions with a relative prediction error of at least 10% than a simple history-based predictor.
“The results reveal that the SVR model yields more predictions with a relative prediction error (RPE) of at least 10% than a simple history-based predictor.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [238]
Several ML-based approaches have addressed limitations of inferring congestion by estimating throughput, RTT, and mobility in TCP-based networks, table entries rate in NDNs, and congestion level in DTNs.
“For the aforementioned reasons, several ML-based approaches have addressed the limitations of inferring the congestion in various network architectures by estimating different network parameters: throughput [238, 316, 371], RTT [22,128], and mobility [309] in TCP-based networks, table entries rate in NDNs [230], and congestion level in DTNs [412].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [238]
El Khayat et al. find that Timeout Loss Rate adds significant improvement in prediction accuracy.
“From the different features used in the learning phase for building the ML models, the authors find that the Timeout Loss Rate (TLR) adds significant improvement in prediction accuracy.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [238]
MLP-NN achieve the lowest accuracy error, followed by MART.
“MLP-NN achieve the lowest accuracy error, followed by MART with [332].”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [238]
TCP, as well as some multicast and multipath protocols, infer the congestion state from estimated network parameters such as RTT and throughput to adapt their behavior.
“TCP, as well as some multicast and multipath protocols, infer the congestion state from estimated network parameters (e.g.RTT and throughput) to adapt their behavior.”
From A comprehensive survey on machine learning for networking: Evolution, applications and research opportunities (2018) · cites this paper as [238]
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- Authors
- El Khayat, I., Geurts, P., & Leduc, G.
- Affiliations
- University of Liège (BE)
- Corresponding author
- Leduc, G.
- ORCID
- Geurts, P.
- Venue
- Computer Networks
- Publisher
- Elsevier BV
- Published
- 2007-07
- Volume
- 51
- Issue
- 10
- Pages
- 2631-2644
- ISSN
- 1389-1286 (ISSN-L)
- OpenAlex
- W2081504788
- Cited by
- 10
- References
- 41
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 2 · 2021: 1 · 2018: 1 · 2016: 1 · 2015: 1
- FWCI
- 0.96
- Type
- journal article
- Language
- en
- Open access
- green
- orbi.uliege.be
- Licence
- Locations
- Computer Networks (publishedVersion)
- Open Repository and Bibliography (University of Liège) (submittedVersion, other-oa)
- Funders
- European Commission
- Fonds De La Recherche Scientifique - FNRS
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Networks and Protocols
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- TCP throughput, analytical throughput model, TCP-friendliness, packet loss detection
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