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Record W2119609284 · doi:10.1109/ccece.2004.1349656

The performance of TCP congestion control algorithm over high-speed transmission links

2004· article· en· W2119609284 on OpenAlexaff
Z. Chen, M. Mehmet Ali

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceComputer networkCUBIC TCPTCP Friendly Rate ControlTransmission Control ProtocolTCP global synchronizationNetwork congestionThroughputTCP accelerationTCP tuningExplicit Congestion NotificationTCP Westwood plusTransmission (telecommunications)Random early detectionQueueNetwork packetActive queue managementWirelessTelecommunications

Abstract

fetched live from OpenAlex

In the Internet, many applications use TCP (Transmission Control Protocol) as the main transport protocol. TCP congestion control algorithm has proved to be inadequate as the speed of the transmission links increases and users demand higher throughput. The main feature of TCP congestion control algorithm is its additive increase/multiplicative decrease (AIMD) property. AIMD requires very low packet loss rates, which is not possible with the present optical transmission links, in order to achieve higher throughputs. The proposed solutions for this problem fail to protect bandwidth share of the low throughput users. We propose that low and high throughput user traffic is stored in separate queues and the two queues are served according to the weighted-round-robin (WRR) service discipline. The simulation results show that this improves the performance of TCP over high-speed links and preserves fairness to the low-throughput users.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.197
Teacher spread0.193 · 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 teacher head, 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

Citations2
Published2004
Admission routes1
Has abstractyes

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