David A. Monge

22 papers B 1C 1Journal 13Unranked 7
YearRankTypeTitle / Venue / Authors
2025 J jnl
Reinforcement Learning initialization by evolutionary formulation: Application for workflow autoscaling in the Cloud.
Eng. Appl. Artif. Intell.
Luciano Robino, Yisel Gari, Elina Pacini, Cristian Mateos, Virginia Yannibelli, David A. Monge
2025 — conf
Reinforcement Learning-based Cloud Autoscaler Initialization via Evolutionary Algorithms.
GECCO Companion
Luciano Robino, Elina Pacini, Yisel Gari, Cristian Mateos, Virginia Yannibelli, David A. Monge
2024 J jnl
Online RL-based cloud autoscaling for scientific workflows: Evaluation of Q-Learning and SARSA.
Future Gener. Comput. Syst.
Yisel Gari, Elina Pacini, Luciano Robino, Cristian Mateos, David A. Monge
2022 J jnl
A Q-learning approach for the autoscaling of scientific workflows in the Cloud.
Future Gener. Comput. Syst.
Yisel Gari, David A. Monge, Cristian Mateos
2021 — conf
Endowing the MIA Cloud Autoscaler with Adaptive Evolutionary and Particle Swarm Multi-Objective Optimization Algorithms.
MICAI (1)
Virginia Yannibelli, Elina Pacini, David A. Monge, Cristian Mateos, Guillermo Rodríguez
2021 J jnl
Reinforcement learning-based application Autoscaling in the Cloud: A survey.
Eng. Appl. Artif. Intell.
Yisel Gari, David A. Monge, Elina Pacini, Cristian Mateos, Carlos García Garino
2020 J jnl
A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud.
Sci. Program.
Virginia Yannibelli, Elina Pacini, David A. Monge, Cristian Mateos, Guillermo Rodríguez
2020 — conf
An NSGA-III-Based Multi-objective Intelligent Autoscaler for Executing Engineering Applications in Cloud Infrastructures.
MICAI (1)
Virginia Yannibelli, Elina Pacini, David A. Monge, Cristian Mateos, Guillermo Rodríguez
2020 J jnl
CMI: An online multi-objective genetic autoscaler for scientific and engineering workflows in cloud infrastructures with unreliable virtual machines.
J. Netw. Comput. Appl.
David A. Monge, Elina Pacini, Cristian Mateos, Enrique Alba, Carlos García Garino
2020 J jnl
Learning budget assignment policies for autoscaling scientific workflows in the cloud.
Clust. Comput.
Yisel Gari, David A. Monge, Cristian Mateos, Carlos García Garino
2020 J jnl
Reinforcement Learning-based Autoscaling of Workflows in the Cloud: A Survey.
CoRR
Yisel Gari, David A. Monge, Elina Pacini, Cristian Mateos, Carlos García Garino
2018 J jnl
CMI: An Online Multi-objective Genetic Autoscaler for Scientific and Engineering Workflows in Cloud Infrastructures with Unreliable Virtual Machines.
CoRR
David A. Monge, Elina Pacini, Cristian Mateos, Enrique Alba, Carlos García Garino
2018 J jnl
Meta-heuristic based autoscaling of cloud-based parameter sweep experiments with unreliable virtual machines instances.
Comput. Electr. Eng.
David A. Monge, Elina Pacini, Cristian Mateos, Carlos García Garino
2017 C conf
A performance comparison of data-aware heuristics for scheduling jobs in mobile grids.
CLEI
Matías Hirsch, Cristian Mateos, Juan Manuel Rodriguez, Alejandro Zunino, Yisel Gari, David A. Monge
2017 J jnl
Autoscaling scientific workflows on the cloud by combining on-demand and spot instances.
Comput. Syst. Sci. Eng.
David A. Monge, Yisel Gari, Cristian Mateos, Carlos García Garino
2017 — conf
FaaSter, Better, Cheaper: The Prospect of Serverless Scientific Computing and HPC.
CARLA
Josef Spillner, Cristian Mateos, David A. Monge
2017 — conf
Markov Decision Process to Dynamically Adapt Spots Instances Ratio on the Autoscaling of Scientific Workflows in the Cloud.
CARLA
Yisel Gari, David A. Monge, Cristian Mateos, Carlos García Garino
2015 J jnl
Ensemble learning of runtime prediction models for gene-expression analysis workflows.
Clust. Comput.
David A. Monge, Matej Holec, Filip Zelezný, Carlos García Garino
2014 — conf
Adaptive Spot-Instances Aware Autoscaling for Scientific Workflows on the Cloud.
CARLA
David A. Monge, Carlos García Garino
2014 — conf
Ensemble Learning of Run-Time Prediction Models for Data-Intensive Scientific Workflows.
CARLA
David A. Monge, Matej Holec, Filip Zelezný, Carlos García Garino
2014 J jnl
LOGOS: Enabling Local Resource Managers for the Efficient Support of Data-Intensive Workflows within Grid Sites.
Comput. Informatics
David A. Monge, Carlos García Garino
2011 B conf
Template-based semi-automatic workflow construction for gene expression data analysis.
CBMS
Jiri Belohradsky, David A. Monge, Filip Zelezný, Matej Holec, Carlos García Garino