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About

About

I am Associate Professor with Habilitation at the Informatics Department of University of Minho, where I teach Fault-Tolerant Distributed Systems. I serve as a member of the Board of Directors of INESC TEC, director of the Minho Advanced Computing Centre, and co-director of the UT Austin Portugal program. I am the national representative to the EuroHPC JU.

I received my PhD. degree in 2000 from the École Polytechnique Fédérale de Lausanne. My main research contributions have been in the field of fault-tolerant distributed agreement and epidemic communication algorithms and in the conception, development and assessment of dependable database systems. I have coordinated the H2020 SafeCloud project on secure processing in the Cloud and the FP6 GORDA project on open database replication, two previous national projects on scalable dependable databases, ESCADA and StrongRep, and the U. Minho team in the FP7 CumuloNimbo and LeanBigdata projects. I published over 100 research papers on large scale and dependable distributed systems, and served on the programme committee of several highly reputed conferences. I chaired the PC of IFIP DAIS and IEEE SRDS, and served as general chair of SRDS and ACM Eurosys.

Details

Details

  • Name

    Rui Carlos Oliveira
  • Role

    Diretor
  • Since

    01st November 2011
010
Publications

2023

TADA: A Toolkit for Approximate Distributed Agreement

Authors
da Conceição, EL; Alonso, AN; Oliveira, RC; Pereira, JO;

Publication
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

Abstract

2023

Soteria: Preserving Privacy in Distributed Machine Learning

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

Publication
38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023

Abstract
We propose Soteria, a system for distributed privacy-preserving Machine Learning (ML) that leverages Trusted Execution Environments (e.g. Intel SGX) to run code in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The conducted experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41%, when compared to previous related work. Our protocol is accompanied by a security proof, as well as a discussion regarding resilience against a wide spectrum of ML attacks.

2023

Diagnosing applications' I/O behavior through system call observability

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

Publication
2023 53RD ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS, DSN-W

Abstract
We present DIO, a generic tool for observing inefficient and erroneous I/O interactions between applications and in-kernel storage systems that lead to performance, dependability, and correctness issues. DIO facilitates the analysis and enables near real-time visualization of complex I/O patterns for data-intensive applications generating millions of storage requests. This is achieved by non-intrusively intercepting system calls, enriching collected data with relevant context, and providing timely analysis and visualization for traced events. We demonstrate its usefulness by analyzing two production-level applications. Results show that DIO enables diagnosing resource contention in multi-threaded I/O that leads to high tail latency and erroneous file accesses that cause data loss.

2023

Toward a Practical and Timely Diagnosis of Application's I/O Behavior

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

Publication
IEEE ACCESS

Abstract
We present DIO, a generic tool for observing inefficient and erroneous I/O interactions between applications and in-kernel storage backends that lead to performance, dependability, and correctness issues. DIO eases the analysis and enables near real-time visualization of complex I/O patterns for data-intensive applications generating millions of storage requests. This is achieved by non-intrusively intercepting system calls, enriching collected data with relevant context, and providing timely analysis and visualization for traced events. We demonstrate its usefulness by analyzing four production-level applications. Results show that DIO enables diagnosing inefficient I/O patterns that lead to poor application performance, unexpected and redundant I/O calls caused by high-level libraries, resource contention in multithreaded I/O that leads to high tail latency, and erroneous file accesses that cause data loss. Moreover, through a detailed evaluation, we show that, when comparing DIO's inline diagnosis pipeline with a similar state-of-the-art solution, our system captures up to 28x more events while keeping tracing performance overhead between 14% and 51%.

2023

LOOM: A Closed-Box Disaggregated Database System

Authors
Coelho, F; Alonso, AN; Ferreira, L; Pereira, J; Oliveira, R;

Publication
PROCEEDINGS OF12TH LATIN-AMERICAN SYMPOSIUM ON DEPENDABLE AND SECURE COMPUTING, LADC 2023

Abstract
Cloud native database systems provide highly available and scalable services as part of cloud platforms by transparently replicating and partitioning data across automatically managed resources. Some systems, such as Google Spanner, are designed and implemented from scratch. Others, such as Amazon Aurora, derive from traditional database systems for better compatibility but disaggregate storage to cloud services. Unfortunately, because they follow an open-box approach and fork the original code base, they are difficult to implement and maintain. We address this problem with Loom, a replicated and partitioned database system built on top of PostgreSQL that delegates durable storage to a distributed log native to the cloud. Unlike previous disaggregation proposals, Loom is a closed-box approach that uses the original server through existing interfaces to simplify implementation and improve robustness and maintainability. Experimental evaluation achieves 6x higher throughput and 5x lower response time than standard replication and competes with the state of the art in cloud and HPC hardware.

Supervised
thesis

2022

Otimização do Stock de Consumíveis e Peças de Reserva: Automatização das Encomendas com Base em Modelos Preditivos

Author
José Miguel Borges Balio Duarte Pinto

Institution
UP-FEUP

2020

Robotic Bin Picking of Entangled Tubes

Author
Gonçalo da Mota Laranjeira Torres Leão

Institution
UP-FEUP

2020

Drill-down Dashboard for Coordination of Master Programmes in Engineering

Author
Anabela Costa e Silva

Institution
UP-FEUP

2019

An EDSL for Modeling Kidney Exchange Programs

Author
João Paulo Rocha Viana

Institution
UP-FCUP

2019

Exploratory Analysis of Meteorological Data

Author
Joel Agostinho Nunes Pinto de Sousa

Institution
UP-FCUP