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Sobre

Francisco Cruz nasceu em Portugal (1986), tem um B.Sc. (2007), um M.Sc. (2009) e doutoramento (2016) pela Universidade do Minho e atualmente é investigador de pós-doutoramento no INESC TEC.

Durante o seu mestrado começou a trabalhar no HASLab / INESC TEC, na Universidade do Minho, no projeto financiado pelo HPLabs Innovation Research DC2MS - Serviços Confiáveis de Gerenciamento de Computação em Nuvem (DC2MS - IRA / CW118736). Durante esse período, a sua investigação centrou-se em ambientes "Cloud Computing", mais especificamente nas novas bases de dados NoSQL. Paralelamente, trabalhou na sua tese de mestrado, intitulado SocialSeer, que girou em torno do compartilhamento de metadados e sugestão de dados em sistemas do tipo dropbox.

No seu doutoramento o seu foco de investigação mudou para fornecer uma interface SQL em bases de dados NoSQL, bem como em melhorar a sua elasticidade e desempenho.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Francisco Miguel Cruz
  • Cargo

    Investigador Colaborador Externo
  • Desde

    01 janeiro 2012
  • Nacionalidade

    Portugal
  • Contactos

    +351253604440
    francisco.m.cruz@inesctec.pt
003
Publicações

2016

Resource Usage Prediction in Distributed Key-Value Datastores

Autores
Cruz, F; Maia, F; Matos, M; Oliveira, R; Paulo, J; Pereira, J; Vilaca, R;

Publicação
DISTRIBUTED APPLICATIONS AND INTEROPERABLE SYSTEMS, DAIS 2016

Abstract
In order to attain the promises of the Cloud Computing paradigm, systems need to be able to transparently adapt to environment changes. Such behavior benefits from the ability to predict those changes in order to handle them seamlessly. In this paper, we present a mechanism to accurately predict the resource usage of distributed key-value datastores. Our mechanism requires offline training but, in contrast with other approaches, it is sufficient to run it only once per hardware configuration and subsequently use it for online prediction of database performance under any circumstance. The mechanism accurately estimates the database resource usage for any request distribution with an average accuracy of 94 %, only by knowing two parameters: (i) cache hit ratio; and (ii) incoming throughput. Both input values can be observed in real time or synthesized for request allocation decisions. This novel approach is sufficiently simple and generic, while simultaneously being suitable for other practical applications.

2016

Towards Performance Prediction in Massive Scale Datastores

Autores
Cruz, F; Coelho, F; Oliveira, R;

Publicação
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND SERVICES SCIENCE, VOL 1 (CLOSER)

Abstract
Buffer caching mechanisms are paramount to improve the performance of today's massive scale NoSQL databases. In this work, we show that in fact there is a direct and univocal relationship between the resource usage and the cache hit ratio in NoSQL databases. In addition, this relationship can be leveraged to build a mechanism that is able to estimate resource usage of the nodes composing the NoSQL cluster.

2015

CumuloNimbo: A Cloud Scalable Multi-tier SQL Database

Autores
Peris, RJ; Martínez, MP; Kemme, B; Brondino, I; Pereira, JO; Vilaça, R; Cruz, F; Oliveira, R; Ahmad, MY;

Publicação
IEEE Data Eng. Bull.

Abstract

2014

PH1: A transactional middleware for NoSQL

Autores
Coelho, F; Cruz, F; Vilaca, R; Pereira, J; Oliveira, R;

Publicação
Proceedings of the IEEE Symposium on Reliable Distributed Systems

Abstract
NoSQL databases opt not to offer important abstractions traditionally found in relational databases in order to achieve high levels of scalability and availability: transactional guarantees and strong data consistency. In this work we propose pH1, a generic middleware layer over NoSQL databases that offers transactional guarantees with Snapshot Isolation. This is achieved in a non-intrusive manner, requiring no modifications to servers and no native support for multiple versions. Instead, the transactional context is achieved by means of a multiversion distributed cache and an external transaction certifier, exposed by extending the client's interface with transaction bracketing primitives. We validate and evaluate pH1 with Apache Cassandra and Hyperdex. First, using the YCSB benchmark, we show that the cost of providing ACID guarantees to these NoSQL databases amounts to 11% decrease in throughput. Moreover, using the transaction intensive TPC-C workload, pH1 presented an impact of 22% decrease in throughput. This contrasts with OMID, a previous proposal that takes advantage of HBase's support for multiple versions, with a throughput penalty of 76% in the same conditions © 2014 IEEE.

2014

Workload-aware table splitting for NoSQL

Autores
Cruz, F; Maia, F; Oliveira, R; Vilaca, R;

Publicação
Proceedings of the ACM Symposium on Applied Computing

Abstract
Massive scale data stores, which exhibit highly desirable scalability and availability properties are becoming pivotal systems in nowadays infrastructures. Scalability achieved by these data stores is anchored on data independence; there is no clear relationship between data, and atomic inter-node operations are not a concern. Such assumption over data allows aggressive data partitioning. In particular, data tables are horizontally partitioned and spread across nodes for load balancing. However, in current versions of these data stores, partitioning is either a manual process or automated but simply based on table size. We argue that size based partitioning does not lead to acceptable load balancing as it ignores data access patterns, namely data hotspots. Moreover, manual data partitioning is cumbersome and typically infeasible in large scale scenarios. In this paper we propose an automated table splitting mechanism that takes into account the system workload. We evaluate such mechanism showing that it simple, non-intrusive and effective. Copyright 2014 ACM.