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Record W2050687058 · doi:10.1145/1082983.1083081

Policies, grids and autonomic computing

2005· article· en· W2050687058 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM SIGSOFT Software Engineering Notes · 2005
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceAutonomic computingContext (archaeology)Resource allocationGrid computingGridResource management (computing)Work (physics)Resource (disambiguation)Service levelService (business)Risk analysis (engineering)Process managementManagement scienceKnowledge managementDistributed computingBusinessEngineeringCloud computing

Abstract

fetched live from OpenAlex

The goals of resource management fall within the overall aims of autonomic and grid computing, namely the sharing of resources automatically, and the allocation of resources depending on both application and business needs. Resource allocation can be guided by policies which encapsulate decisions made by the management system. Policies can be used to encapsulate many different types of management decisions including possible corrective actions when a performance requirement of an application is not being satisfied and actions to take place when there is more demand then supply. System policy is derived from the interactions between Service Level Agreements (contractual agreements between businesses) and locally specified management rules. This paper explores the potential use of mathematical models (e.g., optimisation models) for relating the various types of policies. It describes the current and proposed work in applying policies to resource management in the context of autonomic and grid computing systems.

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.

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)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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