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Detalhes

Detalhes

  • Nome

    Rui Carlos Oliveira
  • Cargo

    Diretor
  • Desde

    01 novembro 2011
010
Publicações

2023

TADA: A Toolkit for Approximate Distributed Agreement

Autores
da Conceiçao, EL; Alonso, AN; Oliveira, RC; Pereira, JO;

Publicação
DISTRIBUTED APPLICATIONS AND INTEROPERABLE SYSTEMS, DAIS 2023

Abstract
Approximate agreement has long been relegated to the sidelines compared to exact consensus, with its most notable application being clock synchronisation. Other proposed applications stemming from control theory target multi-agent consensus, namely for sensor stabilisation, coordination in robotics, and trust estimation. Several proposals for approximate agreement follow the Mean Subsequence Reduce approach, simply applying different functions at each phase. However, taking clock synchronisation as an example, applications do not fit neatly into the MSR model: Instead they require adapting the algorithms' internals. Our contribution is two-fold. First, we identify additional configuration points, establishing a more general template of MSR approximate agreement algorithms. We then show how this allows us to implement not only generic algorithms but also those tailored for specific purposes (clock synchronisation). Second, we propose a toolkit for making approximate agreement practical, providing classical implementations as well as allow these to be configured for specific purposes. We validate the implementation with classical algorithms and clock synchronisation.

2023

Soteria: Preserving Privacy in Distributed Machine Learning

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

Publicação
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

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

Publicação
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

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

Publicação
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

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

Publicação
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.

Teses
supervisionadas

2022

Accelerating Deep Learning Training on High-Performance Computing with Storage Tiering

Autor
Marco Filipe Leitão Dantas

Instituição
UM

2022

Implementação de métodos práticos e seguros para o armazenamento de chaves criptográficas em aplicações

Autor
João Nuno Alves Lopes

Instituição
UM

2022

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

Autor
Tânia da Conceição Araújo Esteves

Instituição
UM

2021

Implementation of practical and secure methods for storage of cryptographic keys in applications

Autor
João Nuno Alves Lopes

Instituição
UM

2021

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

Autor
Tânia da Conceição Araújo Esteves

Instituição
UM