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Record W2044353310 · doi:10.1109/wcnc.2010.5506357

Boundedness of Heterogeneous TCP Flows with Multiple Bottlenecks

2010· article· en· W2044353310 on OpenAlexaff
Hongtao Zhang, Lin Cai, Xinzhi Liu, Xuemin Shen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of VictoriaUniversity of Waterloo
Fundersnot available
KeywordsActive queue managementCorrectnessComputer scienceNetwork congestionQueueing theoryTCP Friendly Rate ControlTCP global synchronizationTransmission Control ProtocolThroughputQueueFlow (mathematics)Computer networkFlow control (data)Stability (learning theory)Distributed computingNetwork packetMathematicsAlgorithmTelecommunications

Abstract

fetched live from OpenAlex

TCP has been the dominant congestion control protocol in the Internet. Although it is well known that TCP combined with intermediate systems with active queue management (AQM) schemes can not guarantee asymptotical stability when the feedback delay or the link capacity is large, the asymptotical stability may not be necessary for network achieving good performance in terms of resource utilization, flow throughput and queueing delay. Practical bounds are important performance index for congestion control protocols and AQM schemes. How to derive practical bounds for realistic systems with heterogeneous flows, various delays and multiple bottlenecks is an important and challenging open issue. In this paper, we study the boundedness of generalized TCP and AQM systems considering the heterogeneity of flows and the impact of multiple bottlenecks. We derive the uniform bounds and uniform ultimate bounds of flow window size, which reveal how the system and flow parameters affect the system performance. Extensive simulation results have been given to verify the correctness of the bounds.

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 score0.968
Threshold uncertainty score0.312

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 head0.192
Teacher spread0.187 · 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
Published2010
Admission routes1
Has abstractyes

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