Modelling large-scale scientific data transfers

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Detalles Bibliográficos
Autor Principal: Bogado García, Joaquín Ignacio
Otros autores o Colaboradores: Lassnig, Mario (Director/a), Díaz, Francisco Javier (Director/a), Monticelli, Fernando (Asesor/a científico/a)
Formato: Tesis
Lengua:inglés
Datos de publicación: 2021
Temas:
ARCHIVOS
datos científicos
Acceso en línea:http://catalogo.info.unlp.edu.ar/meran/getDocument.pl?id=2423
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Descripción Física:1 archivo (3,4 MB) : il. col.
Tabla de Contenidos:
  • 1 Introduction
  • 1.1 Motivation
  • 1.2 Research questions
  • 1.3 Research outline
  • 2 The distributed data management environment
  • 2.1 The World LHC Computing Grid
  • 2.2 The File Transfer Service
  • 2.3 Rucio
  • 2.3.1 Rucio Data IDentiers
  • 2.3.2 Rucio Storage Elements
  • 2.3.3 Replication rules and subscriptions
  • 2.3.4 Replica management and transfers
  • 3 Data selection and model metrics
  • 3.1 Rucio data extraction and selection
  • 3.1.1 Transfers and Deletions
  • 3.1.2 FTS Server
  • 3.1.3 TAPE activities
  • 3.1.4 Failed transfers
  • 3.1.5 Data extraction and treatment
  • 3.2 Metric election
  • 3.2.1 MSE and RMSE
  • 3.2.2 MEA and MedAE
  • 3.2.3 MSLE and RMSLE
  • 3.2.4 Explained Variance and R2 Score
  • 3.2.5 Mean Tweedie Deviance
  • 3.2.6 MAPE and RE
  • 3.2.7 FoGP
  • 3.2.8 Metrics comparison experiment
  • 4 Model of intra-rule Rule TTC extrapolation
  • 4.1 Transfers per rule distribution
  • 4.2 The α and α0 models
  • 4.3 Evaluation of results
  • 5 Model of Rule TTC based on time series analysis
  • 5.1 Problem framing
  • 5.2 The β models
  • 5.3 The γ models
  • 6 Model of Rule TTC based on deep neural networks
  • 6.1 The δn Model
  • 6.2 The δννn Model
  • 6.3 Comparison of the models performance
  • 7 Network time to predict Transfer TTC and Rule TTC
  • 7.1 Network Time for a single transfer
  • 7.2 Network Time as a Transfer TTC and Rule TTC estimator
  • 7.3 Results
  • 8 FTS Queue Time to predict Transfer TTC and Rule TTC
  • 8.1 FTS queue modeling
  • 8.2 Modeling the FTS queue from Rucio data
  • 8.3 Using FTS Queue Time as a Transfer TTC and a Rule TTC
  • predictor
  • 9 Results and conclusion
  • 9.1 Models summary
  • 9.2 Model κ
  • 9.3 Model α
  • 9.4 Models β(t0, ρ) and β∗(t0, ρ)
  • 9.5 Model γ(t0, ρ, λ, ψ, ω)
  • 9.6 Models δ and δνν
  • 9.7 Models based on individual transfers
  • 9.8 Conclusion and nal remarks
  • 10 Future work
  • 10.1 Possible extensions to the δνν model
  • 10.2 More complex auto-regressive models

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