Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Aceitar Rejeitar
  • Menu
Publicações

Publicações por Ricardo Pereira Vilaça

2016

Holistic Shuffler for the Parallel Processing of SQL Window Functions

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

Publicação
DISTRIBUTED APPLICATIONS AND INTEROPERABLE SYSTEMS, DAIS 2016

Abstract
Window functions are a sub-class of analytical operators that allow data to be handled in a derived view of a given relation, while taking into account their neighboring tuples. Currently, systems bypass parallelization opportunities which become especially relevant when considering Big Data as data is naturally partitioned. We present a shuffling technique to improve the parallel execution of window functions when data is naturally partitioned when the query holds a partitioning clause that does not match the natural partitioning of the relation. We evaluated this technique with a non-cumulative ranking function and we were able to reduce data transfer among parallel workers in 85% when compared to a naive approach.

2016

Reducing Data Transfer in Parallel Processing of SQL Window Functions

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

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

Abstract
Window functions are a sub-class of analytical operators that allow data to be handled in a derived view of a given relation, while taking into account their neighboring tuples. We propose a technique that can be used in the parallel execution of this operator when data is naturally partitioned. The proposed method benefits the cases where the required partitioning is not the natural partitioning employed. Preliminary evaluation shows that we are able to limit data transfer among parallel workers to 14% of the registered transfer when using a naive approach.

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

SafeRegions: Performance evaluation of multi-party protocols on HBase

Autores
Pontes, R; Maia, F; Paulo, J; Vilaca, R;

Publicação
2016 IEEE 35TH INTERNATIONAL SYMPOSIUM ON RELIABLE DISTRIBUTED SYSTEMS WORKSHOPS (SRDSW)

Abstract
On-line applications and services are now a critical part of our everyday life. Using these services typically requires us to trust our personal or company's information to a large number of third-party entities. These entities enforce several security measures to avoid unauthorized accesses but data is still stored on common database systems that are designed without data privacy concerns in mind. As a result, data is vulnerable against anyone with direct access to the database, which may be external attackers, malicious insiders, spies or even subpoenas. Building strong data privacy mechanisms on top of common database systems is possible but has a significant impact on the system's resources, computational capabilities and performance. Notably, the amount of useful computation that may be done over strongly encrypted data is close to none, which defeats the purpose of offloading computation to third-party services. In this paper, we propose to shift the need to trust in the honesty and security of service providers to simply trust that they will not collude. This is reasonable as cloud providers, being competitors, do not share data among themselves. We focus on NoSQL databases and present SafeRegions, a novel prototype of a distributed and secure NoSQL database that is built on top of HBase and that guarantees strong data privacy while still providing most of HBase's query capabilities. SafeRegions relies on secret sharing and multiparty computation techniques to provide a NoSQL database built on top of multiple, non-colluding service providers that appear as a single one to the user. Strikingly, service providers, individually, cannot disclose any of the user's data but, together, are able to offer data storage and processing capabilities. Additionally, we evaluate SafeRegions exposing performance trade-offs imposed by security mechanisms and provide useful insights for future research on performance optimization.

2013

DATAFLASKS: an epidemic dependable key-value substrate

Autores
Maia, F; Matos, M; Vilaca, R; Pereira, J; Oliveira, R; Riviere, E;

Publicação
2013 43RD ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN)

Abstract
Recently, tuple-stores have become pivotal structures in many information systems. Their ability to handle large datasets makes them important in an era with unprecedented amounts of data being produced and exchanged. However, these tuple-stores typically rely on structured peer-to-peer protocols which assume moderately stable environments. Such assumption does not always hold for very large scale systems sized in the scale of thousands of machines. In this paper we present a novel approach to the design of a tuple-store. Our approach follows a stratified design based on an unstructured substrate. We focus on this substrate and how the use of epidemic protocols allow reaching high dependability and scalability.

2014

DATAFLASKS: Epidemic store for massive scale systems

Autores
Maia, F; Matos, M; Vilaca, R; Pereira, J; Oliveira, R; Riviere, E;

Publicação
Proceedings of the IEEE Symposium on Reliable Distributed Systems

Abstract
Very large scale distributed systems provide some of the most interesting research challenges while at the same time being increasingly required by nowadays applications. The escalation in the amount of connected devices and data being produced and exchanged, demands new data management systems. Although new data stores are continuously being proposed, they are not suitable for very large scale environments. The high levels of churn and constant dynamics found in very large scale systems demand robust, proactive and unstructured approaches to data management. In this paper we propose a novel data store solely based on epidemic (or gossip-based) protocols. It leverages the capacity of these protocols to provide data persistence guarantees even in highly dynamic, massive scale systems. We provide an open source prototype of the data store and correspondent evaluation. © 2014 IEEE.

  • 1
  • 4