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Record W2003408990 · doi:10.1002/dac.573

Using TCP rate control for queue input‐output rate matching

2002· article· en· W2003408990 on OpenAlexaff
James Aweya, Michel Ouellette, Delfin Y. Montuno

Bibliographic record

VenueInternational Journal of Communication Systems · 2002
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsNortel (Canada)
Fundersnot available
KeywordsComputer scienceTCP global synchronizationTCP Friendly Rate ControlComputer networkTCP Westwood plusTCP accelerationTCP tuningCUBIC TCPNetwork packetZeta-TCPTCP delayed acknowledgmentCompound TCPNetwork congestionWindow (computing)Real-time computingOperating system

Abstract

fetched live from OpenAlex

Abstract In explicit TCP rate control, the receiver's advertised window size in acknowledgment (ACK) packets can be modified by intermediate network elements to reflect network congestion conditions. The TCP receiver's advertised window (i.e. the receive buffer of a TCP connection) limits the maximum window and consequently the throughput that can be achieved by the sender. Appropriate reduction of the advertised window can control the number of packets allowed to be sent from a TCP source. This paper evaluates the performance of a TCP rate control scheme in which the receiver's advertised window size in ACK packets are modified in a network node in order to match the generated load to the assigned bandwidth in the node. Using simulation and performance metrics such as the packet loss rates and the cumulative number of TCP timeouts, we examine the service improvement provided by the TCP rate control scheme to the users. The modified advertised windows computed in the network elements and the link utilization are also examined. Copyright © 2002 John Wiley & Sons, Ltd.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.054
GPT teacher head0.296
Teacher spread0.242 · 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
Published2002
Admission routes1
Has abstractyes

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