MétaCan
Menu
Back to cohort
Record W2165740957 · doi:10.1109/bss.1997.658922

A RAM-based generic packet switch with scheduling capability

2002· article· en· W2165740957 on OpenAlexaff
Massoud Reza Hashemi, Alberto Leon‐Garcia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInterconnection Networks and Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceNetwork packetComputer networkQueueing theoryScheduling (production processes)Packet switchingFair queuingUnicastCut-through switchingEmbedded systemComputer hardwareBurst switchingRound-robin schedulingTransmission delayDynamic priority schedulingQuality of service

Abstract

fetched live from OpenAlex

A generic hardware solution is introduced for switching variable-length packets. The switch can be used in a multiprotocol environment including IP and ATM protocols. In this architecture the packets and/or cells are stored in a shared RAM memory as the main storage resource in the switch, and similar to RAM-based, shared-buffer ATM switch, for each packet a fixed-length minicell is given to a queue controller which provides queueing and scheduling. A bank of sequencer circuits can be used as the controller. We introduce a new version of the single-queue switch, as a compact controller to replace the bank of sequencers with a single sequencer, with the same speed and size. Fair queueing scheduling is implemented by the controller. Other schemes can also be implemented using the sequencer circuit, such as priority queueing. Plug-in modules handle the link and network (routing) protocols. Therefore, these modules can be easily changed or replaced. Multiple protocol modules are also possible in this way. High speed switching is possible in this architecture because the original packets and cells and also the minicells are handled by hardware. The hardware also provides a multicasting capability.

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 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 score0.830
Threshold uncertainty score0.312

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 head0.029
GPT teacher head0.214
Teacher spread0.185 · 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 teacher head, not a consensus.

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

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

Citations2
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicInterconnection Networks and SystemsFrench-language works237,207