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Sobre

O Fábio nasceu em Lisboa, Portugal em 1988. Licenciou-se em Engenharia de Redes de Computadores e Multimédia em 2011 pelo Instituto Superior de Engenharia de Lisboa. Decidiu depois prosseguir os seus estudos, tendo ingressado no Mestrado em Engenharia Informática da Universidade do Minho, de onde obteve o grau de Mestre em 2013. Desde essa altura, o Fábio é invetigador no HASLab, Laboratório Associado do INESC TEC. Doutorou-se em 2018 no programa doutoral em informática MAP-i administrado em co-tutela pelas  Universidades do Minho, Aveiro e Porto. Conjuntamente, o seu trabalho de investigação e tese de doutoramento focam-se em ferramentas de "Data Analytics" para sistemas de larga escala, vulgo "BigData". De entre outros tópicos, o Fábio interessa-se também por sistemas de "Benchmarking" e por sistemas de processamento transacional distribuídos. Nos seus tempos livres, gosta de viajar e de fotografia.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Fábio André Coelho
  • Cluster

    Informática
  • Cargo

    Investigador Auxiliar
  • Desde

    01 janeiro 2014
005
Publicações

2021

Functional Scalability and Replicability Analysis for Smart Grid Functions: The InteGrid Project Approach

Autores
Menci, SP; Bessa, RJ; Herndler, B; Korner, C; Rao, B; Leimgruber, F; Madureira, AA; Rua, D; Coelho, F; Silva, JV; Andrade, JR; Sampaio, G; Teixeira, H; Simões, M; Viana, J; Oliveira, L; Castro, D; Krisper, U; André, R;

Publicação
Energies

Abstract
The evolution of the electrical power sector due to the advances in digitalization, decarbonization and decentralization has led to the increase in challenges within the current distribution network. Therefore, there is an increased need to analyze the impact of the smart grid and its implemented solutions in order to address these challenges at the earliest stage, i.e., during the pilot phase and before large-scale deployment and mass adoption. Therefore, this paper presents the scalability and replicability analysis conducted within the European project InteGrid. Within the project, innovative solutions are proposed and tested in real demonstration sites (Portugal, Slovenia, and Sweden) to enable the DSO as a market facilitator and to assess the impact of the scalability and replicability of these solutions when integrated into the network. The analysis presents a total of three clusters where the impact of several integrated smart tools is analyzed alongside future large scale scenarios. These large scale scenarios envision significant penetration of distributed energy resources, increased network dimensions, large pools of flexibility, and prosumers. The replicability is analyzed through different types of networks, locations (country-wise), or time (daily). In addition, a simple replication path based on a step by step approach is proposed as a guideline to replicate the smart functions associated with each of the clusters.

2020

Self-tunable DBMS Replication with Reinforcement Learning

Autores
Ferreira, L; Coelho, F; Pereira, J;

Publicação
Distributed Applications and Interoperable Systems - Lecture Notes in Computer Science

Abstract

2019

Towards Intra-Datacentre High-Availability in CloudDBAppliance

Autores
Ferreira, L; Coelho, F; Alonso, AN; Pereira, J;

Publicação
Proceedings of the 9th International Conference on Cloud Computing and Services Science

Abstract

2019

Recovery in CloudDBAppliance’s High-availability Middleware

Autores
Abreu, H; Ferreira, L; Coelho, F; Alonso, AN; Pereira, J;

Publicação
Proceedings of the 8th International Conference on Data Science, Technology and Applications

Abstract

2019

Minha: Large-scale distributed systems testing made practical

Autores
Machado, N; Maia, F; Neves, F; Coelho, F; Pereira, J;

Publicação
Leibniz International Proceedings in Informatics, LIPIcs

Abstract
Testing large-scale distributed system software is still far from practical as the sheer scale needed and the inherent non-determinism make it very expensive to deploy and use realistically large environments, even with cloud computing and state-of-the-art automation. Moreover, observing global states without disturbing the system under test is itself difficult. This is particularly troubling as the gap between distributed algorithms and their implementations can easily introduce subtle bugs that are disclosed only with suitably large scale tests. We address this challenge with Minha, a framework that virtualizes multiple JVM instances in a single JVM, thus simulating a distributed environment where each host runs on a separate machine, accessing dedicated network and CPU resources. The key contributions are the ability to run off-the-shelf concurrent and distributed JVM bytecode programs while at the same time scaling up to thousands of virtual nodes; and enabling global observation within standard software testing frameworks. Our experiments with two distributed systems show the usefulness of Minha in disclosing errors, evaluating global properties, and in scaling tests orders of magnitude with the same hardware resources. © Nuno Machado, Francisco Maia, Francisco Neves, Fábio Coelho, and José Pereira; licensed under Creative Commons License CC-BY 23rd International Conference on Principles of Distributed Systems (OPODIS 2019).

Teses
supervisionadas

2020

Automatic Parameter Tuning Using Reinforcement Learning

Autor
Luís Manuel Meruje Ferreira

Instituição
UM

2019

High Availability Architecture for Cloud Based Databases

Autor
Hugo Miguel Ferreira Abreu

Instituição
UM

2018

Mecanismos RDMA para dados colunares em ambientes analíticos

Autor
José Miguel Ribeiro da Silva

Instituição
UM

2018

Armazenamento de Dados Colunar para Processamento Analítico

Autor
Daniel Filipe Vilar Tavares

Instituição
UM