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Sobre

Atualmente é investigador sénior no HASLab e MACC, Universidade do Minho e INESC TEC, trabalhando em computação de alto desempenho em sistemas paralelos e distribuídos. Ele obteve o Ph.D no MAP-i em Ciências da Computação em 2012. Tem uma sólida formação em sistemas distribuídos e gestão de dados em grande escala e cerca de 15 anos de experiência em projetos de pesquisa nacionais e internacionais em sistemas distribuídos: seguros e processamento de consultas em grande escala, computação em nuvem, bancos de dados NoSQL e SQL e replicação de banco de dados. Ele trabalhou em vários projetos de pesquisa europeus CloudDBAppliance, CrowdHealth, BigDataStack, VineYard, CoherentPaaS e LeanBigData, CumuloNimbo, Gorda. Co-orientou vários bolsistas de pesquisa e teses de mestrado e atualmente é co-orientador de 2 alunos de doutorado. Ele publicou trabalhos de pesquisa em sistemas distribuídos confiáveis ??e de grande escala e atuou como revisor em várias conferências de grande renome, como Eurosys, SRDS, Middleware, DSN, OPODIS, LADC, DAIS. Ele também criou e atuou como chair do workshop WPSDS.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ricardo Pereira Vilaça
  • Cluster

    Informática
  • Cargo

    Investigador Auxiliar
  • Desde

    01 novembro 2011
003
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

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.

2021

CODBS: A cascading oblivious search protocol optimized for real-world relational database indexes

Autores
Pontes, R; Portela, B; Barbosa, M; Vilaca, R;

Publicação
2021 40TH INTERNATIONAL SYMPOSIUM ON RELIABLE DISTRIBUTED SYSTEMS (SRDS 2021)

Abstract

Teses
supervisionadas

2020

Trade-offs between privacy and efficiency on databases

Autor
Rogério António da Costa Pontes

Instituição
UP-FCUP

2019

Trade-offs between privacy and efficiency on databases

Autor
Rogério António da Costa Pontes

Instituição
UP-FCUP

2018

Video Based tracking for 3D Scene Analysis

Autor
Américo José Rodrigues Pereira

Instituição
UP-FEUP

2016

Metodologias de calibração de projetores para aplicações robóticas de projection mapping

Autor
Marta Ventura e Costa

Instituição
UP-FEUP

2016

Holistic performance and scalability analysis for large scale distributed systems

Autor
Francisco Nuno Teixeira Neves

Instituição
UM