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Record W2002881589 · doi:10.1109/icc.2010.5502453

An API-RCP Design Using Pole Placement Technique

2010· article· en· W2002881589 on OpenAlexaff
Ou Yang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceRouterBandwidth (computing)Settling timeTransient (computer programming)Protocol (science)Control theory (sociology)Real-time computingStep responseComputer networkControl (management)Control engineeringEngineeringOperating system

Abstract

fetched live from OpenAlex

As an explicit congestion control protocol, API-RCP (Adaptive PI rate control protocol) was originally proposed for best-effort traffic control in the IP networks with high bandwidth-delay product. The original API-RCP adopts a truncated network model for IP router in order to simplify the control system design while still achieving various designed performance measures at steady-state. A further benefit can be realized if one can specify a desirable transient system response (e.g., a shorter settling time). We have achieved this by using a pole-placement technique on a non-truncated closed-loop network model along with proper assignments of the damping ratio and the undamped natural frequency. Our OPNET simulations demonstrate a satisfactory transient network performance as designed. We also make performance comparison between API-RCP and TCP/RED. In the sequel, we can verify the effectiveness of the original truncated model using theoretical analysis instead of numerous simulations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.021
GPT teacher head0.261
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2010
Admission routes1
Has abstractyes

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