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Record W2060393691 · doi:10.1109/icws.2005.19

A study of service composition with QoS management

2005· article· en· W2060393691 on OpenAlexaff
Casey Fung, Patrick C. K. Hung, G. Wang, R.C. Linger, Gwendolyn H. Walton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsQuality of serviceComputer scienceService compositionComposition (language)Service (business)Mobile QoSComputer networkBusinessService delivery framework

Abstract

fetched live from OpenAlex

Quality of service (QoS) management in compositions of services requires careful consideration of QoS characteristics of the services and effective QoS management in their execution. A Web service is a software system that supports interoperable application-to-application interaction over the Internet. Web services are based on a set of XML standards such as simple object access protocol (SOAP). The interactions of SOAP messages between Web services form the theoretical model of SOAP message exchange patterns (MEP). Web Services Business Process Execution Language (WSBPEL) defines an interoperable integration model that facilitates automated process integration in intra- and inter-corporate environments. A service-level agreement (SLA) is a formal contract between a Web services requestor and provider guaranteeing quantifiable issues at defined levels only through mutual concessions. Based on a prior research work on message detail record (MDR), this paper further proposes a SOAP message tracking model for supporting QoS end-to-end management in the context of WSBPEL and SLA. This paper motivates the study of QoS management in a Web service composition framework with the evolution of a distributed toolkit in an industrial setting.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.219
Teacher spread0.212 · 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 designObservational
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

Citations13
Published2005
Admission routes1
Has abstractyes

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