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Record W2118009608 · doi:10.1109/lcn.2010.5735761

A distributed parallel approach for BGP routing table partitioning in next generation routers

2010· article· en· W2118009608 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNetwork Packet Processing and Optimization
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsComputer scienceRouting tableScalabilityBorder Gateway ProtocolDefault-free zoneComputer networkDistributed computingRouting (electronic design automation)Static routingRouting protocolParallel computingOperating system

Abstract

fetched live from OpenAlex

The rapid growth of routing tables represents a major challenge facing the scalability of BGP and indeed the whole Internet infrastructure. In this paper, we introduce a novel distributed algorithmic scheme for partitioning the BGP routing table on multiple controller cards, where we exploit parallelism to enhance both the lookup speed and the scalability of the RIB (Routing Information Base). The proposed scheme increases the lookup performance by letting unrelated tasks, such as the Best Match Prefix (BMP) lookup and the BGP decision process to be executed in parallel at different controller cards. Simulations show that our proposal outperforms classical central lookup mechanisms with a reasonably acceptable cost, while it increases considerably the space scalability of the BGP routing table.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.526
Threshold uncertainty score0.492

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 communication0.0010.001
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 head0.040
GPT teacher head0.249
Teacher spread0.210 · 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