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On the Energy Proportionality of Distributed NoSQL Data Stores

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

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.

References

  1. Apache Cassandra. http://cassandra.apache.org/

  2. Intel 64 and IA-32 Software Developer Manuals - Volume 3. www.intel.com/content/www/us/en/processors/architectures-software-developer-manuals.html

  3. Yahoo Cloud Serving Benchmark (YCSB). https://github.com/brianfrankcooper/YCSB/wiki

  4. Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. IEEE Comput. 40(12), 33–37 (2007)

    Article  Google Scholar 

  5. Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Fikes, R.E.: A distributed storage system for structured data. ACM Trans. Comput. Syst. 26(2), 26 (2008)

    Article  Google Scholar 

  6. David, H., Gorbatov, E., Hanebutte, U.R., Khanna, R., Le, C.: RAPL: memory power estimation and capping. In: International Symposium on Low Power Electronics and Design, ISLPED (2010)

    Google Scholar 

  7. Deng, Q., Meisner, D., Bhattacharjee, A., Wenisch, T.F., Bianchini, R.: CoScale: coordinating CPU and memory system DVFS in server systems. In: International Symposium on Microarchitecture, MICRO (2012)

    Google Scholar 

  8. Deng, Q., Meisner, D., Bhattacharjee, A., Wenisch, T.F., Bianchini, R.: Multiscale: memory system DVFS with multiple memory controllers. In: International Symposium on Low Power Electronics and Design, ISLPED (2012)

    Google Scholar 

  9. Deng, Q., Meisner, D., Ramos, L., Wenisch, T. F., Bianchini, R.: Memscale: Active low-power modes for main memory (2011)

    Google Scholar 

  10. Fan, X., Weber, W.-D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. In: International Symposium on Computer Architecture, ISCA (2007)

    Google Scholar 

  11. Lakshman, A., Malik, P.: Cassandra: a decentralized structured storage system. SIGOPS Operating Syst. Rev. 44(2), 35–40 (2010)

    Article  Google Scholar 

  12. Lang, W., Harizopoulos, S., Patel, J.M., Shah, M.A., Tsirogiannis, D.: Towards energy-efficient database cluster design. arXiv:1208.1933 [cs], August 2012

  13. Li, X., Gupta, R., Adve, S.V., Zhou, Y.: Cross-component energy management: joint adaptation of processor and memory. ACM Trans. Archit. Code Optim. 4(3), 14 (2007)

    Article  Google Scholar 

  14. Mars, J., Tang, L., Hundt, R., Skadron, K., Soffa, M.L.: Bubble-up: increasing utilization in modern warehouse scale computers via sensible co-locations. In: International Symposium on Microarchitecture, MICRO (2011)

    Google Scholar 

  15. Ryckbosch, F., Polfliet, S., Eeckhout, L.: Trends in server energy proportionality. IEEE Comput. 9, 69–72 (2011)

    Article  Google Scholar 

  16. Sarood, O., Langer, A., Kale, L., Rountree, B., Supinski, B.: Optimizing power allocation to CPU and memory subsystems in overprovisioned HPC systems. In: Proceedings of IEEE Cluster (2013)

    Google Scholar 

  17. Sivasubramanian, S.: Amazon dynamoDB: a seamlessly scalable non-relational database service. In: Proceedings of the International Conference on Management of Data, SIGMOD (2012)

    Google Scholar 

  18. Subramaniam, B., Feng, W.: Towards energy-proportional computing for enterprise-class server workloads. In: Proceedings of the International Conference on Performance Engineering, ICPE (2013)

    Google Scholar 

  19. Subramaniam, B., Feng, W.: Enabling efficient power provisioning for enterprise applications. In: Proceedings of the International Symposium on Cluster, Cloud and Grid Computing, CCGRID (2014)

    Google Scholar 

  20. Tsirogiannis, D., Harizopoulos, S., Shah, M.A.: Analyzing the energy efficiency of a database server. In: Proceedings of the International Conference on Management of Data, SIGMOD 2010 (2010)

    Google Scholar 

  21. Wong, D., Annavaram, M.: KnightShift: scaling the energy proportionality wall through server-level heterogeneity. In: Proceedings of the International Symposium on Microarchitecture, MICRO (2012)

    Google Scholar 

  22. Wong, D., Annavaram, M.: Implications of high energy proportional servers on cluster-wide energy proportionality. In: Proceedings of the International Symposium on High Performance Computer Architecture, HPCA (2014)

    Google Scholar 

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Correspondence to Balaji Subramaniam .

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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

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