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Resilient packet ring

2008· book-chapter· en· W828099387 on OpenAlexaff
Martin Maier

Bibliographic record

VenueCambridge University Press eBooks · 2008
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsComputer networkSynchronous optical networkingEthernetBandwidth (computing)Network packetComputer scienceDynamic bandwidth allocationEngineering

Abstract

fetched live from OpenAlex

The IEEE standard 802.17 Resilient Packet Ring (RPR) aims at combining SONET/SDH's carrier-class functionalities of high availability, reliability, and profitable TDM service (voice) support with Ethernet's high bandwidth utilization, low equipment cost, and simplicity (Davik et al., 2004; Yuan et al., 2004; Spadaro et al., 2004). RPR is a ring-based architecture consisting of two counter directional optical fiber rings with up to 255 nodes. Similar to SONET/SDH, RPR is able to provide fast recovery from a single link or node failure within 50 ms, and carry legacy TDM traffic with a high level of quality of service (QoS). Similar to Ethernet, RPR provides advantages of low equipment cost and simplicity and exhibits an improved bandwidth utilization due to statistical multiplexing. The bandwidth utilization is further increased by means of spatial reuse. In RPR, packets are removed from the ring by the corresponding destination node (destination stripping). The destination stripping enables nodes in different ring segments to transmit simultaneously, resulting in spatial reuse of bandwidth and an increased bandwidth utilization. Furthermore, RPR provides fairness, as opposed to today's Ethernet, and allows the full ring bandwidth to be utilized under normal (failure-free) operation conditions, as opposed to today's SONET/SDH rings where 50% of the available bandwidth is reserved for protection. Current RPR networks are single-channel systems (i.e., each fiber ring carries a single wavelength channel) and are expected to be primarily deployed in metro edge and metro core areas. In the following sections, we explain RPR in greater detail, paying particular attention to its architecture, access control, fairness control, and protection.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.016
GPT teacher head0.179
Teacher spread0.163 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2008
Admission routes1
Has abstractyes

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