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Record W1707454109 · doi:10.5555/1516124.1516147

Consistent and scalable cache replication for multi-tier J2EE applications

2007· article· en· W1707454109 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
TopicParallel Computing and Optimization Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer scienceScalabilityReplication (statistics)CacheQuality of serviceDistributed computingComputer networkProtocol (science)Benchmark (surveying)High availabilityDatabase

Abstract

fetched live from OpenAlex

Data centers are the most critical infrastructures of companies and they are demanding higher and higher levels of quality of service (QoS) e.g., availability, scalability... At the core of data centers we find multi-tier architectures providing service to applications. Current infrastructure for multi-tier systems has focused exclusively in providing high availability using replication. Most approaches replicate a single tier, becoming the non-replicated tier a bottleneck and single point of failure. In this paper, we present a novel approach that provides availability and scalability for multi-tier applications. The approach lies in a replicated cache that takes into account both the application server tier (middle-tier) and the database (back-end). The underlying replicated cache protocol fully embeds the replication logic in the application server. The protocol exhibits good scalability as shown by our evaluation based on the new industrial benchmark for J2EE multi-tier systems, SPECjAppServer.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.892
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.049
GPT teacher head0.324
Teacher spread0.275 · 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