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Record W2170753697 · doi:10.1109/tc.2014.2315617

SLA: A Stage-Level Latency Analysisfor Real-Time Communicationin a Pipelined Resource Model

2014· article· en· W2170753697 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Computers · 2014
Typearticle
Languageen
FieldComputer Science
TopicReal-Time Systems Scheduling
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCarbon Management CanadaCanada Foundation for Innovation
KeywordsComputer scienceLatency (audio)Pipeline (software)Parallel computingData transmissionTransmission (telecommunications)Distributed computingComputer networkOperating systemTelecommunications

Abstract

fetched live from OpenAlex

We present a communication analysis for hard real-time systems interconnects. The objective is to provide tight estimates on the worst-case communication latency between communicating processing elements that use a priority-aware communication medium for data transmission. The communication model consists of communication tasks transmitting data across a series of pipelined resources. The analysis incorporates interferences caused by multiple communication tasks requesting the pipelined resources, and it captures parallel transmission of data between multiple pipeline stages. We call this analysis a stage-level analysis. We evaluate the proposed analysis through simulation of synthetic benchmarks, and we apply the analysis to an instantiation of a platform proposed by Shi and Burns. Our experiments confirm that stage-level analysis provides tight upper-bounds when compared to previous work and improves schedulability by 34 percent.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.636
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.030
GPT teacher head0.253
Teacher spread0.223 · 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