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Record W2140121233 · doi:10.1109/ccece.2003.1226202

The effect of reordering and dropping packets on TCP over a slow wireless link

2004· article· en· W2140121233 on OpenAlexaff
A. Nehme, William Phillips, William Robertson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer networkTCP Westwood plusComputer scienceTCP Friendly Rate ControlTCP accelerationTCP global synchronizationZeta-TCPTCP tuningSackTCP delayed acknowledgmentCUBIC TCPNetwork packetTCP WestwoodTransmission Control ProtocolWireless networkTCP VegasNetwork congestionWirelessEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Transmission control protocol (TCP) is used by many standard data application in current wired networks. It has proven to work well in traditional networks where packets losses are mainly due to network congestion. On the other hand, a totally different scenario happens in the wireless link. Due to the high bit error rate (BER), TCP performance is affected. Many corrupted and delayed packets occur in the wireless link that result a poor performance of TCP. There have been a lot of schemes proposed to improve the performance of TCP over wireless networks. This paper discusses the behavior of TCP over a wireless slow link. The effects of dropping and reordering packets are studied. The network is simulated using the network simulator NS (Ref.13), a discrete event simulator targeted at networking research. The modeled link does not suffer from any congestion issues, and there are no intermediate routers where some packets could be dropped due to buffer exhaustion. The behavior and the performance of different TCP enhancement are compared to the new Reno. The TCP enhancements include limiting receiver's advertised window, selective acknowledgment (SACK) option, duplicate selective acknowledgment (D-SACK) option, limited transmit, and initial window of four segments.

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: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.207

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 science0.0000.000
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 head0.004
GPT teacher head0.205
Teacher spread0.202 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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