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Record W2926906848 · doi:10.1109/jsyst.2019.2903819

Fair Congestion Control Protocol for Data Center Bridging

2019· article· en· W2926906848 on OpenAlex
Mahmoud Bahnasy, Halima Elbiaze

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 Systems Journal · 2019
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsUniversité du Québec à MontréalÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceComputer networkNetwork congestionData centerBridging (networking)Network packetDistributed computingPacket lossEthernetLatency (audio)Cloud computingActive queue managementScalabilityOperating system

Abstract

fetched live from OpenAlex

Data center networking brought a new era of data-intensive applications such as remote direct memory access, high-performance computing, and cloud computing, which raise new challenges for network researchers. Such applications require minimum network latency, no packet loss, and fairness between flows. Therefore, IEEE Data Center Bridging Task Group presents several enhancements for Ethernet networks to fulfill these requirements. In this context, we investigate the possibility of achieving dropless Ethernet. We extend our previously proposed congestion control protocol, named Heterogeneous Flow (HetFlow), to achieve minimum queue length and consequently minimum network latency. In addition, we present a mathematical model, stability analysis, and scalability study of the proposed protocol. Further, extensive simulation experiments are conducted to verify our mathematical analysis. Moreover, it is illustrated by simulations that HetFlow improves fairness between flows of different packet sizes and different round trip times.

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)0.001
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 score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.300
Teacher spread0.260 · 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