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Record W2344324982 · doi:10.1109/rtas.2016.7461327

Criticality- and Requirement-Aware Bus Arbitration for Multi-Core Mixed Criticality Systems

2016· article· en· W2344324982 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
TopicReal-Time Systems Scheduling
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsArbiterComputer scienceMixed criticalityAvionicsCriticalityPreemptionArbitrationScheduling (production processes)Offset (computer science)Asynchronous communicationFault toleranceEmbedded systemDistributed computingComputer networkOperating systemEngineering

Abstract

fetched live from OpenAlex

This work presents CArb, an arbiter for controlling accesses to the shared memory bus in multi-core mixed criticality systems. CArb is a requirement-aware arbiter that optimally allocates service to tasks based on their requirements. It is also criticality-aware since it incorporates criticality as a first-class principle in arbitration decisions. CArb supports any number of criticality levels and does not impose any restrictions on mapping tasks to processors. Hence, it operates in tandem with existing processor scheduling policies. In addition, CArb is able to dynamically adapt memory bus arbitration at run time to respond to increases in the monitored execution times of tasks. Utilizing this adaptation, CArb is able to offset these increases; hence, postpones the system need to switch to a degraded mode. We prototype CArb, and evaluate it with an avionics case-study from Honeywell as well as synthetic experiments.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.115
GPT teacher head0.341
Teacher spread0.225 · 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

Quick stats

Citations42
Published2016
Admission routes1
Has abstractyes

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