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Record W2163785351 · doi:10.1109/cnsr.2005.37

Investigating TCP Performance Issues in Satellite Networks

2005· article· en· W2163785351 on OpenAlexaff
Sangeetha Subramanian, S. Sivakumar, William Phillips, William Robertson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsSaint Mary's UniversityDalhousie University
Fundersnot available
KeywordsComputer networkComputer scienceTCP accelerationTCP global synchronizationTCP Friendly Rate ControlTCP tuningZeta-TCPPropagation delaySatellite constellationNetwork delaySatelliteReal-time computingTransmission Control ProtocolEngineeringNetwork packet

Abstract

fetched live from OpenAlex

TCP is the widely used transport protocol across the Internet but it was originally designed for wired networks. In satellite networks, TCP encounters serious problems due to the physical properties of the wireless medium. The high delays in GEO networks and high variability of delay in LEO systems are the most significant factors affecting TCP performance. This paper identifies and illustrates TCP performance issues in satellite links by making a detailed comparison between these two common satellite altitudes. In low altitude satellite constellations, the propagation and switching delays are highly variable because of routing changes and handovers. Previous work on variable delay has focused explicitly on the retransmit timer. This paper makes a flow based analysis of abrupt delay changes to better understand TCP performance in LEO systems. Simulations are performed with the NS 2 satellite extension using the iridium constellation. It is observed that TCP performs better in LEO than in GEO systems because of its lower latency. It is also shown that large receiver buffers and intermediate buffers can alleviate the effect of abrupt delay changes in satellite networks.

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 score0.899
Threshold uncertainty score0.378

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 communication0.0000.001
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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

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