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Record W2170945258 · doi:10.1109/lcn.2008.4664205

Distributed and scalable control plane for next generation routers: A case study of OSPF

2008· article· en· W2170945258 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 institutionsConcordia University
Fundersnot available
KeywordsComputer scienceScalabilityRouterComputer networkForwarding planeDistributed computingCore routerOpen Shortest Path FirstExploitRouting protocolRouting (electronic design automation)Operating systemLink-state routing protocolComputer securityNetwork packet

Abstract

fetched live from OpenAlex

The growing traffic on the core Internet entails new requirements related to scalability and resiliency of the routers. One of the promising trends of router evolution is to build next generation routers with enhanced memory capacity and computing resources, distributed across a very high speed switching fabric. The main limitation of the current routing and signaling software modules, traditionally designed in a centralized manner, is that they do not scale in order to fully exploit such an advanced distributed hardware architecture. This paper discusses an implementation for an OSPF architecture for next generation routers, aiming at increasing the scalability and resiliency. The proposed architecture distributes the OSPF processing functions on router cards, i.e., on both control and line cards. Therefore, it reduces the bottlenecks and improves both the overall performance and the resiliency in the presence of faults. Scalability is estimated with respect to the CPU utilization and memory requirements.

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: Empirical · Consensus signal: none
Teacher disagreement score0.561
Threshold uncertainty score0.215

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 head0.050
GPT teacher head0.250
Teacher spread0.201 · 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