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On the Energy Proportionality of Distributed NoSQL Data Stores

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Book cover High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation (PMBS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8966))

Abstract

The computing community is facing several big data challenges due to the unprecedented growth in the volume and variety of data. Many large-scale Internet companies use distributed NoSQL data stores to mitigate these challenges. These NoSQL data-store installations require massive computing infrastructure, which consume significant amount of energy and contribute to operational costs. This cost is further aggravated by the lack of energy proportionality in servers.

Therefore, in this paper, we study the energy proportionality of servers in the context of a distributed NoSQL data store, namely Apache Cassandra. Towards this goal, we measure the power consumption and performance of a Cassandra cluster. We then use power and resource provisioning techniques to improve the energy proportionality of the cluster and study the feasibility of achieving an energy-proportional data store. Our results show that a hybrid (i.e., power and resource) provisioning technique provides the best power savings — as much as 55 %.

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Notes

  1. 1.

    The other components, denoted by “Others,” also include the power consumption of the hard disk.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Virginia Tech, Blacksburg, USA

    Balaji Subramaniam & Wu-chun Feng

Authors
  1. Balaji Subramaniam
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  2. Wu-chun Feng
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Correspondence to Balaji Subramaniam .

Editor information

Editors and Affiliations

  1. University of Warwick, Coventry, United Kingdom

    Stephen A. Jarvis

  2. University of Warwick, Coventry, United Kingdom

    Steven A. Wright

  3. Sandia National Laboratories CSRI, Albuquerque, New Mexico, USA

    Simon D. Hammond

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Subramaniam, B., Feng, Wc. (2015). On the Energy Proportionality of Distributed NoSQL Data Stores. In: Jarvis, S., Wright, S., Hammond, S. (eds) High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation. PMBS 2014. Lecture Notes in Computer Science(), vol 8966. Springer, Cham. https://doi.org/10.1007/978-3-319-17248-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-17248-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17247-7

  • Online ISBN: 978-3-319-17248-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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