MétaCan
Menu
Back to cohort

A Microservice-Based Architecture for Performance and Energy Benchmarking of Docker-Host Linux Distributions on Internet-of-Things Devices

2021· article· en· W3176568918 on OpenAlexaff
David Lennick, Akramul Azim, Ramiro Liscano

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCloud Computing and Resource Management
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsComputer scienceContainer (type theory)BenchmarkingOperating systemHost (biology)The InternetArchitectureEmbedded systemEngineering

Abstract

fetched live from OpenAlex

Containers are rapidly being adopted in several areas of the information technology industry. A major area is edge and embedded Internet-of-Things systems. In this paper, we present a microservice-based architecture for performance analysis and energy consumption of Internet-of-Things "Docker host" Linux distributions. Our methodology builds on previous container benchmarking work, with analysis of performance metrics such as processing, memory, and disk throughput. Furthermore, our methodology introduces container-engine performance metrics related to container lifecycle operations, and concurrent container performance. We demonstrate by comparing four Linux distributions in this domain: BalenaOS, HypriotOS, RancherOS, and Raspbian Lite. All source code is provided.

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 score0.888
Threshold uncertainty score0.368

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 head0.008
GPT teacher head0.214
Teacher spread0.205 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicCloud Computing and Resource ManagementFrench-language works237,207