Skip to main content

The Search for Energy-Efficient Building Blocks for the Data Center

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNISA,volume 6161)

Abstract

This paper conducts a survey of several small clusters of machines in search of the most energy-efficient data center building block targeting data-intensive computing. We first evaluate the performance and power of single machines from the embedded, mobile, desktop, and server spaces. From this group, we narrow our choices to three system types. We build five-node homogeneous clusters of each type and run Dryad, a distributed execution engine, with a collection of data-intensive workloads to measure the energy consumption per task on each cluster. For this collection of data-intensive workloads, our high-end mobile-class system was, on average, 80% more energy-efficient than a cluster with embedded processors and at least 300% more energy-efficient than a cluster with low-power server processors.

Keywords

  • Energy Efficiency
  • Embed Processor
  • Server Processor
  • Intel Atom
  • Data Center Computing

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-24322-6_15
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-642-24322-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. United States Environmental Protection Agency Energy Star Program: Report on Server and Data Center Energy Efficiency (2007)

    Google Scholar 

  2. Barroso, L.A., Hölzle, U.: The Datacenter as a Computer: an Introduction to the Design of Warehouse-Scale Machines. Morgan-Claypool, San Rafael (2009)

    Google Scholar 

  3. Koomey, J.G.: Estimating Total Power Consumption by Servers in the U.S. and the World. Analytics Press, Oakland (2007)

    Google Scholar 

  4. Poess, M., Nambiar, R.O.: Energy Cost, The Key Challenge of Today’s Data Centers: a Power Consumption Analysis of TPC-C Results. In: Proceedings of the VLDB Endowment, vol. 1(1), pp. 1229–1240 (2008)

    Google Scholar 

  5. Barroso, L.A., Hölzle, U.: The Case for Energy-Proportional Computing. IEEE Computer 40(12), 33–37 (2007)

    CrossRef  Google Scholar 

  6. Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: 6th Symposium on Operating Systems Design and Implementation, pp. 137–150. USENIX, Berkeley (2004)

    Google Scholar 

  7. Hadoop Wiki, http://wiki.apache.org/hadoop/

  8. Isard, M., Budiu, M., Yu, Y., Birrell, A., Fetterly, D.: Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks. In: EuroSys Conference, pp. 59–72. ACM, New York (2007)

    Google Scholar 

  9. Thain, D., Tannenbaum, T., Livny, M.: Distributed Computing in Practice: The Condor Experience. Concurrency and Computation: Practice and Experience 17, 2–4 (2005)

    CrossRef  Google Scholar 

  10. Intel: Intel X18-M/X25-M SATA solid state drive product manual, http://download.intel.com/design/flash/nand/mainstream/mainstream-sata-ssd-datasheet.pdf

  11. Szalay, A.S., Bell, G., Huang, H.H., Terzis, A., White, A.: Low-Power Amdahl-Balanced Blades for Data Intensive Computing. In: 2nd Workshop on Power Aware Computing and Systems (HotPower), ACM SIGOPS(online) (2009)

    Google Scholar 

  12. Caulfield, A.M., Grupp, L.M., Swanson, S.: Gordon: Using Flash Memory to Build Fast, Power-Efficient Clusters for Data-Intensive Applications. In: 14th International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 217–228. ACM, New York (2009)

    Google Scholar 

  13. Andersen, D.G., Franklin, J., Kaminsky, M., Phanishayee, A., Tan, L., Vasudevan, V.: FAWN: a Fast Array of Wimpy Nodes. In: 22nd Symposium on Operating Systems Principles. ACM SIGOPS(online) (2009)

    Google Scholar 

  14. Vasudevan, V., Andersen, D., Kaminsky, M., Tan, L., Franklin, J., Moraru, I.: Energy-Efficient Cluster Computing with FAWN: Workloads and Implications. In: 1st International Conference on Energy-Efficient Computing and Networking (e-Energy), pp. 195–204. ACM, New York (2010)

    CrossRef  Google Scholar 

  15. Beckmann, A., Meyer, U., Sanders, P., Singler, J.: Energy-Efficient Sorting Using Solid State Disks, http://sortbenchmark.org/ecosort_2010_Jan_01.pdf

  16. Reddi, V.J., Lee, B.C., Chilimbi, T.M., Vaid, K.: Web Search Using Mobile Cores: Quantifying and Mitigating the Price of Efficiency. In: 37th International Symposium on Computer Architecture, pp. 314–325. ACM, New York (2010)

    Google Scholar 

  17. Rivoire, S., Shah, M.A., Ranganathan, P., Kozyrakis, C.: JouleSort: A Balanced Energy-Efficiency Benchmark. In: SIGMOD International Conference on Management of Data, pp. 365–376. ACM, New York (2007)

    Google Scholar 

  18. Lim, K.T., Ranganathan, P., Chang, J., Patel, C.D., Mudge, T.N., Reinhardt, S.K.: Understanding and Designing New Server Architectures for Emerging Warehouse-Computing Environments. In: 35th International Symposium on Computer Architecture, pp. 315–326. ACM, New York (2008)

    Google Scholar 

  19. Hamilton, J.: CEMS: Low-Cost, Low-Power Servers for Internet-Scale Services. In: 4th Biennial Conference on Innovative Data Systems Research (online) (2009)

    Google Scholar 

  20. ClueWeb09 dataset, http://boston.lti.cs.cmu.edu/Data/clueweb09/

  21. Schroeder, B., Pinheiro, E., Weber, W.-D.: DRAM Errors in the Wild: A Large-Scale Field Study. In: Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS/Performance), pp. 193–204. ACM, New York (2009)

    Google Scholar 

  22. Yelick, K.: How to Waste a Parallel Computer. In: Keynote Address at 36th International Symposium on Computer Architecture (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Keys, L., Rivoire, S., Davis, J.D. (2011). The Search for Energy-Efficient Building Blocks for the Data Center. In: Varbanescu, A.L., Molnos, A., van Nieuwpoort, R. (eds) Computer Architecture. ISCA 2010. Lecture Notes in Computer Science, vol 6161. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24322-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24322-6_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24321-9

  • Online ISBN: 978-3-642-24322-6

  • eBook Packages: Computer ScienceComputer Science (R0)