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Record W2144748009 · doi:10.1109/bsc.2010.5472999

Performance analysis of modern TCP variants: A comparison of Cubic, Compound and New Reno

2010· article· en· W2144748009 on OpenAlexaff
Imad Abdeljaouad, Houda Rachidi, Stênio Fernandes, A. Karmouch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsGoodputComputer scienceZeta-TCPComputer networkTCP accelerationTCP Westwood plusTCP Friendly Rate ControlTCP VegasTCP tuningTCP global synchronizationTransmission Control ProtocolTCP WestwoodCompound TCPCUBIC TCPH-TCPHSTCPDistributed computingWirelessThroughputNetwork packetTelecommunications

Abstract

fetched live from OpenAlex

TCP is the main and most widely used transport protocol for reliable communication. Because of its widespread need, researchers have been studying and proposing new TCP variants trying to improve its behavior towards congestion to make it use the most available bandwidth while preserving a logical level of fairness towards other protocols. This paper aims at evaluating and comparing the performance of the most recent TCP implementations deployed in popular Operating Systems. We carefully choose scenarios to investigate the goodput, intra- and inter-protocol fairness of these TCP variants. Results show that running Cubic over wired links outperforms Compound and New Reno in the presence of reverse traffic. However, the protocols behave differently over wireless links where they achieve a low goodput with a very small variation. Also, all three variants are fair to other TCP traffic and achieve the same intra-protocol fairness over wireless links.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.253
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2010
Admission routes1
Has abstractyes

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