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Record W7055033322

BGP with an adaptive minimal route advertisement interval

2006· dissertation· en· W7055033322 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSummit (Simon Fraser University) · 2006
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAdvanced Frequency and Time Standards
Canadian institutionsnot available
FundersSimon Fraser University
KeywordsBorder Gateway ProtocolConvergence (economics)Interval (graph theory)Network topologyDuration (music)Protocol (science)Limit (mathematics)Retard
DOInot available

Abstract

fetched live from OpenAlex

The duration of the Minimal Route Advertisement Interval (MRA.1) and the implementation of MRAI timers have a significant influence on the convergence time of the Border Gateway Protocol (BGP). Previous studies have reported existence: of optimal MRAI values that minimize the BGP convergence time for various network topologies and traffic loads. In this thesis, we propose the adaptive MRAI algorithm for adaptive adjustment of MRAI values. We also introduce reusable MRAI timers that limit the number of advertisements for each destination.. The modified BGP is namedl BGP with adaptive MRAI (BGP-AM). BGP-AM perfimnance is evaluated using the BGP processing delay based on reported measurements. ns-2 simulation results d-emonstrate that BGP-AM leads to a shorter convergence time and a number of update messages comparable to the current BGP. Furthermore, BGP-AM convergence time depends linearly on the BGP processing delay.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.720
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.220
Teacher spread0.213 · 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