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About

Francisco Cruz was born in Portugal (1986), has a B.Sc. (2007), an M.Sc. (2009), and PhD (2016) by the University of Minho and is currently postdoctoral researcher at INESC TEC.

During his M.Sc. he started working at HASLab/ INESC TEC, at the University of Minho, on the HPLabs Innovation Research Award funded project DC2MS - Dependable Cloud Computing Management Services (DC2MS - IRA/CW118736). During this time, his research focused on Cloud Computing environments, more specifically on the new NoSQL data stores. At the same time, he worked on his master thesis, entitled SocialSeer, revolved around metadata sharing and suggestion of data in dropbox like systems.

In his Ph.D work his research shifted towards providing a SQL interface to NoSQL data stores as well as how to improve their elasticity and performance.

Interest
Topics
Details

Details

  • Name

    Francisco Miguel Cruz
  • Role

    External Research Collaborator
  • Since

    01st January 2012
  • Nationality

    Portugal
  • Contacts

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

2016

Resource Usage Prediction in Distributed Key-Value Datastores

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

Publication
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

Authors
Cruz, F; Coelho, F; Oliveira, R;

Publication
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

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

Publication
IEEE Data Eng. Bull.

Abstract

2014

PH1: A transactional middleware for NoSQL

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

Publication
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

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

Publication
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.

Supervised
thesis

2018

Study, Selection and Evaluation of an Iot Platform for Data Collection and Analysis for medical sensors

Author
João Pedro Nóbrega Rei

Institution
UM