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International Conference on Quantitative Evaluation of Systems

QEST 2014: Quantitative Evaluation of Systems pp 194–209Cite as

Modelling Replication in NoSQL Datastores

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8657))

Abstract

Distributed NoSQL datastores have been developed to cater for the usage scenarios of Web 2.0 applications. These systems provide high availability through the replication of data across different machines and data centers. The performance characteristics of NoSQL datastores are determined by the degree of data replication and the consistency guarantees required by the application. This paper presents a novel performance study of the Cassandra NoSQL datastore deployed on the Amazon EC2 cloud platform. We show that a queueing Petri net model can scale to represent the characteristics of read workloads for different replication strategies and cluster sizes. We benchmark one Cassandra node and predict response times and throughput for these configurations. We study the relationship between cluster size and consistency guarantees on cluster performance and identify the effect that node capacity and configuration has on the overall performance of the cluster.

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Author information

Authors and Affiliations

  1. Department of Computing, Imperial College London, London, SW7 2AZ, UK

    Rasha Osman

  2. Dip. di Elettronica e Informazione, Politecnico di Milano, via Ponzio 34/5, 20133, Milano, Italy

    Pietro Piazzolla

Authors
  1. Rasha Osman
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  2. Pietro Piazzolla
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Editor information

Editors and Affiliations

  1. School of Computing Science, University of Glasgow, G12 8RZ, Glasgow, UK

    Gethin Norman

  2. University of Illinois, 61801-2918, Urbana, IL, USA

    William Sanders

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© 2014 Springer International Publishing Switzerland

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Osman, R., Piazzolla, P. (2014). Modelling Replication in NoSQL Datastores. In: Norman, G., Sanders, W. (eds) Quantitative Evaluation of Systems. QEST 2014. Lecture Notes in Computer Science, vol 8657. Springer, Cham. https://doi.org/10.1007/978-3-319-10696-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-10696-0_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10695-3

  • Online ISBN: 978-3-319-10696-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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