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Record W2054790797 · doi:10.1109/pccc.2012.6407773

Effect of MRAI timers and routing policies on BGP convergence times

2012· article· en· W2054790797 on OpenAlex
Rajvir Gill, Ravinder Paul, Ljiljana Trajković

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBorder Gateway ProtocolDefault-free zoneComputer scienceComputer networkRouting protocolConvergence (economics)RouterRouting (electronic design automation)The InternetOverhead (engineering)Distributed computingRouting tableStatic routing

Abstract

fetched live from OpenAlex

The Minimal Route Advertisement Interval (MRAI) plays a prominent role in convergence of the Border Gateway Protocol (BGP). Previous studies have suggested using adaptive MRAI and reusable timers to reduce the BGP convergence time. The adaptive MRAI timers perform well under the normal load of BGP updates. However, a large number of BGP updates may flood Internet routers. We propose a new algorithm, MRAI with Flexible Load Dispersing (FLD-MRAI), which reduces the router's overhead by dispersing the load in case of a large number of BGP updates. We also examine the MRAI timers under the normal load of BGP updates. Since BGP routing policies play a significant role in preserving the Internet routing stability, we evaluate their impact on BGP convergence time and Route Flap Damping (RFD) algorithms. The proposed algorithms are evaluated using the ns-BGP network simulator.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score0.236

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 head0.005
GPT teacher head0.235
Teacher spread0.229 · 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

Quick stats

Citations15
Published2012
Admission routes1
Has abstractyes

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