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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
United States Environmental Protection Agency Energy Star Program: Report on Server and Data Center Energy Efficiency (2007)
Barroso, L.A., Hölzle, U.: The Datacenter as a Computer: an Introduction to the Design of Warehouse-Scale Machines. Morgan-Claypool, San Rafael (2009)
Koomey, J.G.: Estimating Total Power Consumption by Servers in the U.S. and the World. Analytics Press, Oakland (2007)
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)
Barroso, L.A., Hölzle, U.: The Case for Energy-Proportional Computing. IEEE Computer 40(12), 33–37 (2007)
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)
Hadoop Wiki, http://wiki.apache.org/hadoop/
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)
Thain, D., Tannenbaum, T., Livny, M.: Distributed Computing in Practice: The Condor Experience. Concurrency and Computation: Practice and Experience 17, 2–4 (2005)
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
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)
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)
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)
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)
Beckmann, A., Meyer, U., Sanders, P., Singler, J.: Energy-Efficient Sorting Using Solid State Disks, http://sortbenchmark.org/ecosort_2010_Jan_01.pdf
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)
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)
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)
Hamilton, J.: CEMS: Low-Cost, Low-Power Servers for Internet-Scale Services. In: 4th Biennial Conference on Innovative Data Systems Research (online) (2009)
ClueWeb09 dataset, http://boston.lti.cs.cmu.edu/Data/clueweb09/
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)
Yelick, K.: How to Waste a Parallel Computer. In: Keynote Address at 36th International Symposium on Computer Architecture (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)