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Sobre

Sobre

Eu sou investigador no HASLab e professor na U. Minho. A minha investigação centra-se em sistemas distribuidos confiáveis. Interesso-me principalmente pela gestão de dados, incluindo replicação de bases de dados e processamento de SQL sobre sistemas NoSQL, e por comunicação em grupo, incluindo protocolos de consenso e de difusão epidémica para sistemas em grande escala. Interesso-me também por técnicas e ferramentas para testar, avaliar e observar sistemas distribuídos confiáveis.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    José Orlando Pereira
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 novembro 2011
006
Publicações

2022

AIDA-DB: A Data Management Architecture for the Edge and Cloud Continuum

Autores
Faria, N; Costa, D; Pereira, J; Vilaça, R; Ferreira, L; Coelho, F;

Publicação
19th IEEE Annual Consumer Communications & Networking Conference, CCNC 2022, Las Vegas, NV, USA, January 8-11, 2022

Abstract

2022

Adaptive database synchronization for an online analytical cioud-to-edge continuum

Autores
Costa, D; Pereira, J; Vilaça, R; Faria, N;

Publicação
SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing, Virtual Event, April 25 - 29, 2022

Abstract

2021

BDUS: implementing block devices in user space

Autores
Faria, A; Macedo, R; Pereira, J; Paulo, J;

Publicação
SYSTOR '21: The 14th ACM International Systems and Storage Conference, Haifa, Israel, June 14-16, 2021.

Abstract

2021

Detailed Black-Box Monitoring of Distributed Systems

Autores
Neves, F; Vilaca, R; Pereira, J;

Publicação
APPLIED COMPUTING REVIEW

Abstract
Modern containerized distributed systems, such as big data storage and processing stacks or micro-service based applications, are inherently hard to monitor and optimize, as resource usage does not directly match hardware resources due to multiple virtualization layers. For instance, inter-application traffic is an important factor in as it directly indicates how components interact, it has not been possible to accurately monitor it in an application independent way and without severe overhead, thus putting it out of reach of cloud platforms. In this paper we present an efficient black-box monitoring approach for gathering detailed structural information of collaborating processes in a distributed system that can be queried for various purposes, as it includes both information about processes, containers, and hosts, as well as resource usage and amount of data exchanged. The key to achieving high detail and low overhead without custom application instrumentation is to use a kernel-aided event driven strategy. We validate a prototype implementation by applying it to multi-platform microservice deployments, evaluate its performance with micro-benchmarks, and demonstrate its usefulness for container placement in a distributed data storage and processing stack (i.e., Cassandra and Spark).

2021

Horus: Non-Intrusive Causal Analysis of Distributed Systems Logs

Autores
Neves, F; Machado, N; Vilaca, R; Pereira, J;

Publicação
51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN 2021)

Abstract
Logs are still the primary resource for debugging distributed systems executions. Complexity and heterogeneity of modern distributed systems, however, make log analysis extremely challenging. First, due to the sheer amount of messages, in which the execution paths of distinct system components appear interleaved. Second, due to unsynchronized physical clocks, simply ordering the log messages by timestamp does not suffice to obtain a causal trace of the execution. To address these issues, we present Horus, a system that enables the refinement of distributed system logs in a causally-consistent and scalable fashion. Horus leverages kernel-level probing to capture events for tracking causality between application-level logs from multiple sources. The events are then encoded as a directed acyclic graph and stored in a graph database, thus allowing the use of rich query languages to reason about runtime behavior. Our case study with TrainTicket, a ticket booking application with 40+ microservices, shows that Horus surpasses current widely-adopted log analysis systems in pinpointing the root cause of anomalies in distributed executions. Also, we show that Horus builds a causally-consistent log of a distributed execution with much higher performance (up to 3 orders of magnitude) and scalability than prior state-of-the-art solutions. Finally, we show that Horus' approach to query causality is up to 30 times faster than graph database built-in traversal algorithms.

Teses
supervisionadas

2021

Otimização de Protocolos de Difusão Espidémica

Autor
Diogo André Teles Fernandes

Instituição
UM

2021

Towards a Dependable and Decentralized Software-Defined Storage Architecture

Autor
Ricardo Gonçalves Macedo

Instituição
UM

2021

Towards Tunable Distributed Data Management for IoT

Autor
Luís Manuel Meruje Ferreira

Instituição
UM

2021

Distributed and Dependable SDS Control Plane for HPC

Autor
Mariana Martins de Sá Miranda

Instituição
UM

2021

Análise e Otimização de Protocolos de Acordo Distribuído aproximado

Autor
Joaquim Manuel Gonçalves Oliveira

Instituição
UM