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Record W2814832

A new distributed security model for Linux Clusters

2004· article· en· W2814832 on OpenAlexaff
Makan Pourzandi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsMount Royal UniversityEricsson (Canada)
Fundersnot available
KeywordsComputer scienceServerComputer security modelAccess controlComputer clusterCluster (spacecraft)Distributed computingNode (physics)Security domainOperating systemComputer securityEngineering
DOInot available

Abstract

fetched live from OpenAlex

With the increasing use of clusters in different domains, efficient and flexible security has now become an essential requirement for clusters, though many security mechanisms exist, there is a need to develop more flexible and coherent security mechanisms for large distributed applications. In this paper, we present the need for a unified cluster wide security space for large distributed applications. Based on these needs, we propose a new security model that implements security zones inside the cluster. The model is an extension to Mandatory Access Control (MAC) mechanisms used at node level to the whole cluster with processes as basic security entities. We designed this model with clustered Linux servers running carrier-grade applications in mind but this model can be used in any domain that needs Linux clusters running large distributed applications continuously with no interruptions. We prove the feasibility of this approach through an open source implementation of the concept [1]. 1

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.007
Open science0.0030.003
Research integrity0.0020.002
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 head0.025
GPT teacher head0.314
Teacher spread0.289 · 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 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

Citations7
Published2004
Admission routes1
Has abstractyes

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