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Record W2581521131 · doi:10.1109/cloudcom.2016.0099

Self-Healing Redundancy for OpenStack Applications through Fault-Tolerant Multi-Agent Task Scheduling

2016· article· en· W2581521131 on OpenAlexaff
Fereydoun Farrahi Moghaddam, Abdelouahed Gherbi, Yves Lemieux

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsÉcole de Technologie SupérieureEricsson (Canada)
Fundersnot available
KeywordsOperating systemComputer scienceVirtual machineFailoverRedundancy (engineering)Embedded systemFault tolerancePlug-in

Abstract

fetched live from OpenAlex

OpenStack does not provide high availability at application and VM (Virtual Machine) level. Some basic functionalities are provided by OpenStack Heat to restart/rebuild the VM/stack if it is not responsive. However, it does not support failover or different types of redundancy models. HAStack is a custom designed OpenStack Heat plugin in form of a multi-agent system which is intended to provide such functionalities by utilizing the PaceMaker (or OpenSAF) and OpenStack Nova API without touching the OpenStack code. These features will ensure HAStack full compatibility with OpenStack and ease of upgrade to a new version of OpenStack.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.904
Threshold uncertainty score0.608

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 communication0.0000.001
Open science0.0010.000
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 head0.035
GPT teacher head0.300
Teacher spread0.265 · 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 designTheoretical or conceptual
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

Citations6
Published2016
Admission routes1
Has abstractyes

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