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Record W4283826070 · doi:10.4230/lipics.disc.2022.36

On Implementing SWMR Registers from SWSR Registers in Systems with Byzantine Failures

2022· preprint· en· W4283826070 on OpenAlexfundno aff
Xing Hu, Sam Toueg

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typepreprint
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImpossibilityComputer scienceByzantine fault toleranceContext (archaeology)Asynchronous communicationProcess (computing)ImplementationCrashMatching (statistics)Theoretical computer scienceFault toleranceDistributed computingProgramming languageMathematicsComputer network

Abstract

fetched live from OpenAlex

The implementation of registers from (potentially) weaker registers is a classical problem in the theory of distributed computing. Since Lamport’s pioneering work [Leslie Lamport, 1986], this problem has been extensively studied in the context of asynchronous processes with crash failures. In this paper, we investigate this problem in the context of Byzantine process failures, with and without process signatures. In particular, we first show a strong impossibility result, namely, that there is no wait-free linearizable implementation of a 1-writer n-reader register from atomic 1-writer (n-1)-reader registers. In fact, this impossibility result holds even if all the processes except the writer are given atomic 1-writer n-reader registers, and even if we assume that the writer can only crash and at most one reader is subject to Byzantine failures. In light of this impossibility result, we give two register implementations. The first one implements a 1-writer n-reader register from atomic 1-writer 1-reader registers. This implementation is linearizable (under any combination of Byzantine process failures), but it is wait-free only under the assumption that the writer is correct or no reader is Byzantine - thus matching the impossibility result. The second implementation assumes process signatures; it is wait-free and linearizable under any number and combination of Byzantine process failures.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0000.001
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 head0.052
GPT teacher head0.189
Teacher spread0.137 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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