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Record W1975806744 · doi:10.1109/services.2014.79

Architectural Designs from Mobile Cloud Computing to Ubiquitous Cloud Computing - Survey

2014· article· en· W1975806744 on OpenAlexaff
Richard K. Lomotey, Ralph Deters

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicIoT and Edge/Fog Computing
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCloud computingComputer scienceMobile cloud computingCloud testingUtility computingDistributed computingMobile deviceScope (computer science)Mobile computingVirtual machineUbiquitous computingResource (disambiguation)Resource allocationCloud computing securityOperating systemComputer network

Abstract

fetched live from OpenAlex

This work explores the design trends on Mobile Cloud Computing (MCC) and the recent shift to Ubiquitous Cloud Computing (UCC). The study shows that efficient architectures for the MCC and UCC systems can be deployed through the employment of techniques such as: Virtual Machine (VM) Architectures and Migration, Data, Services, Resource and Computation Offloading, Partitioning and Parallel Execution, and Resource Allocation. The work is aimed at aiding mobile and cloud computing software engineers to understand the broader scope of the area regarding problem identification and solutions.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.036
GPT teacher head0.273
Teacher spread0.237 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2014
Admission routes1
Has abstractyes

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