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David A. Monge
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Faaster, better, cheaper: The prospect of serverless scientific computing and hpc
J Spillner, C Mateos, DA Monge
High Performance Computing: 4th Latin American Conference, CARLA 2017 …, 2018
1132018
Reinforcement learning-based application autoscaling in the cloud: A survey
Y Garí, DA Monge, E Pacini, C Mateos, CG Garino
Engineering Applications of Artificial Intelligence 102, 104288, 2021
742021
A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud
V Yannibelli, E Pacini, D Monge, C Mateos, G Rodriguez
Scientific Programming 2020, 1-17, 2020
292020
CMI: An online multi-objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines
DA Monge, E Pacini, C Mateos, E Alba, CG Garino
Journal of Network and Computer Applications 149, 102464, 2020
242020
Ensemble learning of runtime prediction models for gene-expression analysis workflows
DA Monge, M Holec, F Železný, CG Garino
Cluster Computing 18, 1317-1329, 2015
232015
Autoscaling Scientific Workflows on the Cloud by Combining On-demand and Spot Instances.
DA Monge, Y Garí, C Mateos, CG Garino
Computer Systems Science & Engineering 32 (4), 2017
192017
Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances
DA Monge, E Pacini, C Mateos, CG Garino
Computers & Electrical Engineering 69, 364-377, 2018
182018
Sensibilidad de resultados del ensayo de tracción simple frente a diferentes tamaños y tipos de imperfecciones
C Careglio, D Monge, E Pacini, C Mateos, A Mirasso, CG Garino
Mecánica Computacional 29 (41), 4181-4197, 2010
172010
Learning budget assignment policies for autoscaling scientific workflows in the cloud
Y Garí, DA Monge, C Mateos, C García Garino
Cluster Computing 23, 87-105, 2020
132020
A performance comparison of data-aware heuristics for scheduling jobs in mobile Grids
M Hirsch, C Mateos, JM Rodriguez, A Zunino, Y Garí, DA Monge
2017 XLIII Latin American Computer Conference (CLEI), 1-8, 2017
112017
Adaptive spot-instances aware autoscaling for scientific workflows on the cloud
DA Monge, C García Garino
High Performance Computing: First HPCLATAM-CLCAR Latin American Joint …, 2014
112014
A Q-learning approach for the autoscaling of scientific workflows in the cloud
Y Gari, DA Monge, C Mateos
Future Generation Computer Systems 127, 168-180, 2022
92022
Ensemble learning of run-time prediction models for data-intensive scientific workflows
DA Monge, M Holec, F Z̆elezný, C García Garino
High Performance Computing: First HPCLATAM-CLCAR Latin American Joint …, 2014
82014
A performance prediction module for workflow scheduling
DA Monge, J Bělohradský, C García Garino, F Železný
IV High-Performance Computing Symposium (HPC 2011)(XL JAIIO, Córdoba, 31 de …, 2011
72011
Markov decision process to dynamically adapt spots instances ratio on the autoscaling of scientific workflows in the cloud
Y Garí, DA Monge, C Mateos, C García Garino
High Performance Computing: 4th Latin American Conference, CARLA 2017 …, 2018
62018
Improving Workflows Execution on DAGMan by a Perfomance-driven Scheduling Tool
DA Monge, C García Garino
High-Performance Computing Symposium (HPC 2010)-JAIIO 39 (UADE, 30 de agosto …, 2010
52010
Logos: Enabling local resource managers for the efficient support of data-intensive workflows within grid sites
DA Monge, CG Garino
Computing and Informatics 33 (1), 109-130, 2014
42014
Computational mechanics software as a service project
C García Garino, ER Pacini Naumovich, DA Monge Bosdari, CA Careglio, ...
ISTEC, 2013
42013
Estudios paramétricos de mecánica de sólidos en entornos de computación distribuida
C Catania, C Careglio, D Monge, P Martinez, A Mirasso, CG Garino
Mecánica Computacional, 1063-1084, 2008
32008
An NSGA-III-Based Multi-objective Intelligent Autoscaler for Executing Engineering Applications in Cloud Infrastructures
V Yannibelli, E Pacini, D Monge, C Mateos, G Rodriguez
Advances in Soft Computing: 19th Mexican International Conference on …, 2020
22020
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