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Record W2099389817

BGP ROUTE FLAP DAMPING ALGORITHMS

2006· dissertation· en· W2099389817 on OpenAlex
Shen Wei

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSummit (Simon Fraser University) · 2006
Typedissertation
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsComputer scienceAlgorithmStability (learning theory)Routing (electronic design automation)Computer networkMachine learning
DOInot available

Abstract

fetched live from OpenAlex

Route flap damping (RFD) is a mechanism used in Border Gatelway Protocol (BGP) to prevent persistent routing oscillations in the Internet. It plays an iinportant role in maintaining the stability of the Internet routing system. RFD works by suppressing routes that flap persistently. Several existing algorithms address the issue of identifying and penalizing route flaps. In this thesis, we compare three such algoritllms: original RFD, selective RFD, and RFD+. We implement these algorithms in ns-2 and evaluate their performance. We also propose two possible improvements to the RFD idgorithms.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.203
Teacher spread0.194 · 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