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dc.creatorOsman, Rasha 
dc.creatorPérez, Juan F. 
dc.creatorCasale, Giuliano
dc.identifier.issnISBN: 978-1-5090-4128-2
dc.identifier.issnEISBN: 978-1-5090-4127-5
dc.description.abstractCloud databases achieve high availability by automatically replicating data on multiple nodes. However, the overhead caused by the replication process can lead to an increase in the mean and variance of transaction response times, causing unforeseen impacts on the offered quality-of-service (QoS). In this paper, we propose a measurement-driven methodology to predict the impact of replication on Database-as-a-Service (DBaaS) environments. Our methodology uses operational data to parameterize a closed queueing network model of the database cluster together with a Markov model that abstracts the dynamic replication process. Experiments on Amazon RDS show that our methodology predicts response time mean and percentiles with errors of just 1% and 15% respectively, and under operational conditions that are significantly different from the ones used for model parameterization. We show that our modeling approach surpasses standard modeling methods and illustrate the applicability of our methodology for automated DBaaS provisioning.
dc.relation.ispartofIEEE International Conference on Software Quality, Reliability and Security (QRS), ISBN: 978-1-5090-4128-2;EISBN: 978-1-5090-4127-5 (2016); pp. 268-297
dc.source2016 IEEE International Conference on Software Quality, Reliability and Security (QRS)
dc.titleQuantifying the impact of replication on the quality-of-service in cloud databases
dc.subject.keywordTime factors
dc.subject.keywordQuality of service
dc.subject.keywordData models
dc.subject.keywordRelational databases
dc.subject.keywordComputational modeling
dc.type.spaparte de Libro
dc.rights.accesoRestringido (Acceso a grupos específicos)
dc.title.TranslatedTitleCuantificar el impacto de la replicación en la calidad del servicio en las bases de datos en la nube
dc.relation.citationTitle2016 IEEE International Conference on Software Quality, Reliability and Security (QRS)

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