activity
20152019
most citedUsing Application Data for SLA-aware Auto-scaling in Cloud Environments

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.DC2019

Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering

Renan Souza, Leonardo Azevedo, Vítor Lourenço +10

Machine Learning (ML) has become essential in several industries. In Computational Science and Engineering (CSE), the complexity of the ML lifecycle comes from the large variety of…

cs.LG2018

DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecast

Eduardo R. Rodrigues, Igor Oliveira, Renato L. F. Cunha +1

Running high-resolution physical models is computationally expensive and essential for many disciplines. Agriculture, transportation, and energy are sectors that depend on high-res…

cs.CY2018

A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast

Igor Oliveira, Renato L. F. Cunha, Bruno Silva +1

Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources. Most solutions for yield…

cs.DC2018

JobPruner: A Machine Learning Assistant for Exploring Parameter Spaces in HPC Applications

Bruno Silva, Marco A. S. Netto, Renato L. F. Cunha

High Performance Computing (HPC) applications are essential for scientists and engineers to create and understand models and their properties. These professionals depend on the exe…

cs.DC20152 cited

Using Application Data for SLA-aware Auto-scaling in Cloud Environments

Andre Abrantes D. P. Souza, Marco A. S. Netto

With the establishment of cloud computing as the environment of choice for most modern applications, auto-scaling is an economic matter of great importance. For applications like s…