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About

About

I am currently an auxiliar professor at University of Minho and senior researcher at INESC TEC. I have obtained a PhD degree in Computer Science from the MAP-i Doctoral Program in Computer Science. Currently, I am working on large scale distributed systems with an emphasis on storage and database systems’ scalability, performance, security and dependability. Also, I am interested on the applicability of this research work for solving complex data management challenges for Cloud Computing and HPC centres.

I am the coordinator of the PAStor PT-UTAustin exploratory project and the “Efficient and Secure Data Management for HPC and Cloud Computing” CENTRA project, while leading INESC TEC’s activities on the Compete2020 BigHPC project and ACTPM PT-UTAustin exploratory project. Also, I have several publications in renowned journals and international conferences (e.g., ACM Computing Surveys, IEEE Transactions on Computers, ACM Transactions on Storage, Eurosys, SRDS, SYSTOR).

For more information you can check my personal web page at https://jtpaulo.github.io, as well as, HASLab's research lines on the topics mentioned above: https://dsr-haslab.github.io and https://dbr-haslab.github.io

Interest
Topics
Details

Details

  • Name

    João Tiago Paulo
  • Role

    Senior Researcher
  • Since

    01st November 2011
006
Publications

2023

Distributed Applications and Interoperable Systems - 23rd IFIP WG 6.1 International Conference, DAIS 2023, Held as Part of the 18th International Federated Conference on Distributed Computing Techniques, DisCoTec 2023, Lisbon, Portugal, June 19-23, 2023, Proceedings

Authors
Martínez, MP; Paulo, J;

Publication
DAIS

Abstract

2023

SOTERIA: Preserving Privacy in Distributed Machine Learning

Authors
Brito, C; Ferreira, P; Portela, B; Oliveira, R; Paulo, J;

Publication
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, SAC 2023, Tallinn, Estonia, March 27-31, 2023

Abstract

2023

Diagnosing applications' I/O behavior through system call observability

Authors
Esteves, T; Macedo, R; Oliveira, R; Paulo, J;

Publication
CoRR

Abstract

2023

PADLL: Taming Metadata-intensive HPC Jobs Through Dynamic, Application-agnostic QoS Control

Authors
Macedo, R; Miranda, M; Tanimura, Y; Haga, J; Ruhela, A; Harrell, SL; Evans, RT; Pereira, J; Paulo, J;

Publication
CoRR

Abstract

2023

Taming Metadata-intensive HPC Jobs Through Dynamic, Application-agnostic QoS Control

Authors
Macedo, R; Miranda, M; Tanimura, Y; Haga, J; Ruhela, A; Harrell, SL; Evans, RT; Pereira, J; Paulo, J;

Publication
2023 IEEE/ACM 23RD INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING, CCGRID

Abstract
Modern I/O applications that run on HPC infrastructures are increasingly becoming read and metadata intensive. However, having multiple applications submitting large amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to overall performance degradation and I/O unfairness. We present PADLL, an application and file system agnostic storage middleware that enables QoS control of data and metadata workflows in HPC storage systems. It adopts ideas from Software-Defined Storage, building data plane stages that mediate and rate limit POSIX requests submitted to the shared file system, and a control plane that holistically coordinates how all I/O workflows are handled. We demonstrate its performance and feasibility under multiple QoS policies using synthetic benchmarks, real-world applications, and traces collected from a production file system. Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization.

Supervised
thesis

2022

LazyFS: A file system for assessing applications data durability

Author
João Pedro Rodrigues Azevedo

Institution
UM

2022

A promoção da Inovação Sustentável no contexto empresarial com base nas ferramentas e técnicas de Design Thinking

Author
Margarida Rosa Pimenta

Institution
UP-FEP

2022

Towards a Privacy-Preserving Distributed Machine Learning Framework

Author
Cláudia Vanessa Martins de Brito

Institution
UM

2022

Towards a Dependable and Decentralized Software-Defined Storage Architecture

Author
Ricardo Gonçalves Macedo

Institution
UM

2022

End-to-End Software-Defined Security for Big Data Ecosystem

Author
Tânia da Conceição Araújo Esteves

Institution
UM