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Record W2064413237 · doi:10.1109/tla.2013.6533970

A Token Based Method for Congestion and Packet Loss Control

2013· article· en· W2064413237 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Latin America Transactions · 2013
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer networkExplicit Congestion NotificationNetwork congestionComputer sciencePacket lossNetwork traffic controlFlow control (data)Token bucketNetwork packetTCP Friendly Rate ControlTCP tuningActive queue management

Abstract

fetched live from OpenAlex

Presently the Internet accommodates simultaneous audio, video, and data traffic. This requires the Internet to guarantee the packet loss thus to control network congestion. A series of protocols have been introduced to supplement the insufficient TCP mechanism for controlling the congestion. As such the Core-Stateless Fair Queuing (CSFQ), Token-Based Congestion Control (TBCC) were designed as open or closed-loop controller respectively to provide the fair best effort service for supervising the per-flow bandwidth consumption. In this paper, Stable Token-Limited Congestion Control (STLCC) is introduced as a new protocol which appends inter-domain congestion control to TBCC and makes the congestion control system stable. STLCC produces a congestion index, pushes the packet loss to the network edge and improves the network performance. Finally, the simple version of STLCC is introduced. It is deployable in the Internet without any IP protocols modifications and preserves also the packet datagram.

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.

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: Methods · Consensus signal: none
Teacher disagreement score0.983
Threshold uncertainty score0.628

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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