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Record W2144606760 · doi:10.1109/ipdps.2004.1302946

Dynamic versus static locking in real-time parallel database systems

2004· article· en· W2144606760 on OpenAlexafffund
Anshul Mittal, Sivarama P. Dandamudi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTwo-phase lockingComputer scienceConcurrency controlSerializabilityDatabase transactionDistributed computingReal-time databaseOptimistic concurrency controlScheduling (production processes)Distributed transactionTransaction processingDistributed databaseDistributed concurrency controlProtocol (science)DatabaseParallel computing

Abstract

fetched live from OpenAlex

Summary form only given. Parallel database systems are capable of providing significant performance gains in terms of transaction processing rates. These gains are realized by running many transactions concurrently. A requirement in real-time transaction scheduling is to complete the transactions within their deadline. Due to its simplicity, two-phase locking (2PL) is one of the most commonly used concurrency control mechanism. Two alternative methods of securing locks in the 2PL protocol are static locking or dynamic locking. We report performance of the two locking variants of the 2PL protocol under various degrees of resource and data contention in a real-time parallel database system.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.921
Threshold uncertainty score0.537

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 communication0.0000.001
Open science0.0010.000
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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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

Citations8
Published2004
Admission routes2
Has abstractyes

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