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Record W2117865835 · doi:10.1109/iwsm.1996.534152

Efficient management data acquisition and run-time control of DCE applications using the OSI management framework

2002· article· en· W2117865835 on OpenAlexaff
Michael Katchabaw, Stephen L. Howard, Hanan Lutfiyya, Michael Bauer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceOSI modelNetwork management applicationNetwork managementFlexibility (engineering)Context (archaeology)Systems managementData managementStructure of Management InformationDistributed managementManagement systemNetwork management stationDistributed computingSoftwareComputer networkNetwork architectureDatabaseOperating systemEngineering

Abstract

fetched live from OpenAlex

The goal of a management system in a distributed computing environment (DCE) is to provide a centralized and coordinated view of an otherwise distributed and heterogeneous collection of hardware and software resources. The management software required to achieve this goal will, within a policy framework, monitor, analyze and control network resources, system services and distributed application programs. In our research, we are investigating the use of the Open System Interconnection (OSI) network management standards as a basis for a generic architecture around which such management systems can be built. We are particularly interested in the suitability and flexibility of the OSI standards in the context of managing application programs. The work described in this paper focusses on the efficiency and dynamic control of management data acquisition and on the run-time control of application programs. We describe a prototype management system which has been developed to explore these issues in the management of DCE applications.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.252
Teacher spread0.227 · 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 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
Published2002
Admission routes1
Has abstractyes

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