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Record W1594176915 · doi:10.1109/cloud.2015.147

EdgeX: Edge Replication for Web Applications

2015· article· en· W1594176915 on OpenAlexaff
Hemant Saxena, Kenneth Salem

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCaching and Content Delivery
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceLatency (audio)Data centerEnhanced Data Rates for GSM EvolutionComputer networkJavaReplication (statistics)Distributed computingDynamic web pageWeb applicationOperating systemWorld Wide WebWeb serviceTelecommunications

Abstract

fetched live from OpenAlex

Global Web applications face the problem of high network latency due to their need to communicate with distant data centers. Many applications use edge networks for caching images, CSS, java script, and other static content in order to avoid some of this network latency. However, for updates and for anything other than static content, communication with the data center is still required, and can dominate application request latencies. One way to address this problem is to push more of the web application, as well the database on which it depends, from the remote data center towards the edge of the network. In this paper, we present preliminary work in this direction. Specifically, we present an edge-aware dynamic data replication architecture for relational database systems supporting Web applications. Our objective is to allow dynamic content to be served from the edge of the network, with low latency.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.164

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 communication0.0000.000
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 head0.064
GPT teacher head0.288
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations11
Published2015
Admission routes1
Has abstractyes

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