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Record W2129022583 · doi:10.1109/dexa.1996.558282

A two-phase commit protocol and its performance

2002· article· en· W2129022583 on OpenAlexaff
B.S. Boutros, Bipin C. Desai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsConcordia University
Fundersnot available
KeywordsTwo-phase commit protocolCommitCompensating transactionDistributed transactionComputer scienceProtocol (science)Database transactionTransaction processingDistributed computingSerializabilityTwo-phase lockingReliability (semiconductor)Database

Abstract

fetched live from OpenAlex

A number of schemes have been used in distributed database systems to coordinate the execution of the subtransactions of a global transaction. We study the performance of a simple modification to the basic two phase commit protocol called Prudent Two Phase Commit protocol. The protocol gives the transaction a second chance before it decides to abort it. This prudent approach prevents a transaction from aborting in case of transient communication failures and hence improves system performance and reliability. To measure the performance of this protocol, we simulate a distributed database system. The performance of this simulated distributed system is measured while using the Prudent, Basic, and Optimistic Two Phase Commit protocols. The results of this simulation study are presented with a discussion and interpretation of the graphs generated by the simulation. The results confirm the improvement in system performance with the Prudent Two Phase Commit Protocol.

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)0.005
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.293
Teacher spread0.262 · 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 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

Citations19
Published2002
Admission routes1
Has abstractyes

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