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Record W2148167250 · doi:10.1109/iwsm.1996.534143

Performance evaluation for distributed system components

2002· article· en· W2148167250 on OpenAlexaff
Petre Dini, Gregor von Bochmann, Raouf Boutaba

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSoftware System Performance and Reliability
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsComputer scienceComponent (thermodynamics)Control reconfigurationDistributed computingPollingProcess (computing)Component-based software engineeringReliability engineeringState (computer science)SoftwareSoftware systemReal-time computingEmbedded systemEngineering

Abstract

fetched live from OpenAlex

The performance evaluation of hardware and software system components is based on statistics that are long views on the behavior of these components. Since system resources may have unexpected behavior, relevant current information becomes useful in the management process of these systems, especially for data gathering, reconfiguration, and fault detection activities. Actually, there are few criteria to property evaluate the current availability of component services within distributed systems. Hence, the management system can not realistically select the most suitable decision for configuration. In this paper, we present a proposal for a continuous evaluation of component behaviour related to state changes. This model is further extended by considering different categories of events concerning the degradation of the operational state or usage state. Our proposals are based on the possibility of computing at the component level, the current availability of this component by continuous evaluation. we introduce a several current availability features and propose formula to compute them. Other events concerning a managed object are classified as warning, critical or outstanding, which leads to a more accurate operational view on a component. Several counter-based events are thresholded to improve predictable reconfiguration decisions concerning the usability of a component. The main goal is to offer to the management system current relevant information which can be used within management policies the flexible polling frequency tuned with respect to the current evaluation, or particular aspects related to dynamic tests within distributed systems. Implementation issues with respect to the standard recommendations within distributed systems are presented. Finally we describe how the reconfiguration management systems can use these features in order to monitor, predict, improve the existing configuration, or accommodate the polling frequency according to several simple criteria.

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)0.005
metaresearch head score (Gemma)0.022
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.046
GPT teacher head0.253
Teacher spread0.207 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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