MétaCan
Menu
Back to cohort
Record W2090963188 · doi:10.1109/infcom.2013.6566741

αRoute: A name based routing scheme for Information Centric Networks

2013· article· en· W2090963188 on OpenAlexaff
Reaz Ahmed, Md. Faizul Bari, Shihabur Rahman Chowdhury, Md. Golam Rabbani, Raouf Boutaba, Bertrand Mathieu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkPolicy-based routingRouting tableStatic routingRouting (electronic design automation)IP forwardingScalabilityMultipath routingThe InternetDynamic Source RoutingDefault-free zoneVirtual routing and forwardingBorder Gateway ProtocolTriangular routingEqual-cost multi-path routingDistributed computingLink-state routing protocolRouting protocolWorld Wide WebDatabase

Abstract

fetched live from OpenAlex

One of the crucial building blocks for Information Centric Networking (ICN) is a name based routing scheme that can route directly on content names instead of IP addresses. However, moving the address space from IP addresses to content names brings scalability issues to a whole new level, due to two reasons. First, name aggregation is not as trivial a task as the IP address aggregation in BGP routing. Second, the number of addressable contents in the Internet is several orders of magnitude higher than the number of IP addresses. With the current size of the Internet, name based, anycast routing is very challenging specially when routing efficiency is of prime importance. We propose a novel name-based routing scheme (αRoute) for ICN that offers efficient bandwidth usage, guaranteed content lookup and scalable routing table size.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.014
GPT teacher head0.209
Teacher spread0.195 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicCaching and Content DeliveryFrench-language works237,207