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Record W3049272805 · doi:10.1109/tsp49548.2020.9163512

A Performance Evaluation of TCP BBRv2 Alpha

2020· article· en· W3049272805 on OpenAlexaff
Jose Gomez, Elie Kfoury, Jorge Crichigno, Elias Bou‐Harb, Gautam Srivastava

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsBrandon University
Fundersnot available
KeywordsBottleneckComputer scienceBandwidth (computing)Network congestionH-TCPQueueing theoryComputer networkLatency (audio)HSTCPBandwidth allocationTCP Friendly Rate ControlDistributed computingTCP accelerationTelecommunicationsNetwork packetEmbedded system

Abstract

fetched live from OpenAlex

The alpha version of Bottleneck Bandwidth and Round-trip Time version 2 (BBRv2) has been recently presented, which aims to mitigate the shortcomings of its predecessor, BBR version 1 (BBRv1). Previous studies show that BBRv1 provides a high link utilization and low queuing delay by estimating the available bottleneck bandwidth. However, its aggressiveness induces unfairness when flows i) use different congestion control algorithms, such as CUBIC, and ii) have distinct round-trip times (RTTs). This paper presents an experimental evaluation of BBRv2, using Mininet. Results show that the coexistence between BBRv2-CUBIC is enhanced with respect to that of BBRv1-CUBIC, as measured by the fairness index. They also show that BBRv2 mitigates the RTT unfairness problem observed in BBRv1. Additionally, BBRv2 achieves a better fair share of the bandwidth than its predecessor when network conditions such as bandwidth and latency dynamically change. Results also indicate that the average flow completion time of concurrent flows is reduced when BBRv2 is used.

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)0.006
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 score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.041
GPT teacher head0.248
Teacher spread0.207 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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Same topicNetwork Traffic and Congestion ControlFrench-language works237,207