Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases

Rasha Osman; Juan F. Perez; Giuliano Casale

doi:10.1109/QRS.2016.40

Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases

Rasha Osman, Juan F. Perez, Giuliano Casale

Research output: Chapter in Book/Report › Conference contribution

2 Scopus citations

Abstract

Cloud 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.

Translated title of the contribution	Cuantificación del impacto de la replicación en la calidad del servicio en bases de datos en la nube
Original language	English (US)
Title of host publication	Proceedings - 2016 IEEE International Conference on Software Quality, Reliability and Security, QRS 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	286-297
Number of pages	12
ISBN (Electronic)	9781509041275
DOIs	https://doi.org/10.1109/QRS.2016.40
State	Published - Oct 12 2016
Externally published	Yes
Event	2nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2016 - Vienna, Austria Duration: Aug 1 2016 → Aug 3 2016

Conference

Conference	2nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2016
Country/Territory	Austria
City	Vienna
Period	8/1/16 → 8/3/16

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Software
Safety, Risk, Reliability and Quality

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10.1109/QRS.2016.40

Cite this

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title = "Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases",

abstract = "Cloud 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.",

author = "Rasha Osman and Perez, {Juan F.} and Giuliano Casale",

year = "2016",

month = oct,

day = "12",

doi = "10.1109/QRS.2016.40",

language = "English (US)",

pages = "286--297",

booktitle = "Proceedings - 2016 IEEE International Conference on Software Quality, Reliability and Security, QRS 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2016 ; Conference date: 01-08-2016 Through 03-08-2016",

}

Osman, R, Perez, JF & Casale, G 2016, Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases. in Proceedings - 2016 IEEE International Conference on Software Quality, Reliability and Security, QRS 2016., 7589808, Institute of Electrical and Electronics Engineers Inc., pp. 286-297, 2nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2016, Vienna, Austria, 8/1/16. https://doi.org/10.1109/QRS.2016.40

Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases. / Osman, Rasha; Perez, Juan F.; Casale, Giuliano.
Proceedings - 2016 IEEE International Conference on Software Quality, Reliability and Security, QRS 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 286-297 7589808.

Research output: Chapter in Book/Report › Conference contribution

TY - GEN

T1 - Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases

AU - Osman, Rasha

AU - Perez, Juan F.

AU - Casale, Giuliano

PY - 2016/10/12

Y1 - 2016/10/12

N2 - Cloud 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.

AB - Cloud 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.

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M3 - Conference contribution

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BT - Proceedings - 2016 IEEE International Conference on Software Quality, Reliability and Security, QRS 2016

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 1 August 2016 through 3 August 2016

ER -

Quantifying the Impact of Replication on the Quality-of-Service in Cloud Databases

Abstract

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All Science Journal Classification (ASJC) codes

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