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Record W2569922802 · doi:10.1109/cic.2016.033

A Programming Language and System for Heterogeneous Cloud of Things

2016· article· en· W2569922802 on OpenAlexafffund
Robert Wenger, Xiru Zhu, Jayanth Krishnamurthy, Muthucumaru Maheswaran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceCloud computingCompilerDistributed computingProgramming paradigmInternet of ThingsMobile deviceKey (lock)ArchitectureOperating systemEmbedded systemProgramming language

Abstract

fetched live from OpenAlex

Cloud of Things (CoT) is a computing model that combines the widely popular cloud computing with Internet of Things (IoT). One of the major problems with CoT is the latency of accessing distant cloud resources from the devices, where the data is captured. To address this problem, paradigms such as fog computing and Cloudlets have been proposed to interpose another layer of computing between the clouds and devices. Such a three-layered cloud-fog-device computing architecture is touted as the most suitable approach for deploying many next generation ubiquitous computing applications. Programming applications to run on such a platform is quite challenging because disconnections between the different layers are bound to happen in a large-scale CoT system, where the devices can be mobile. This paper presents a programming language and system for a three-layered CoT system. We illustrate how our language and system addresses some of the key challenges in the three-layered CoT. A proof-of-concept prototype compiler and runtime have been implemented and several example applications are developed using it.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.150

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 designOther design
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

Citations12
Published2016
Admission routes2
Has abstractyes

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