Abstract
Current data centers consume huge amount of power to face the increasing network traffic. Therefore energy efficient processors are required that can process the cloud applications efficiently without consuming excessive power. This paper presents a performance evaluation of the processors that are mainly used in high performance embedded systems in the domain of cloud computing. Several representative applications based on the widely used MapReduce framework are mapped in the embedded processor and are evaluated in terms of performance and energy efficiency. The results shows that high performance embedded processors can achieve up to 7.8x better energy efficiency than the current general purpose processors in typical MapReduce applications.
This is a preview of subscription content, log in via an institution to check access.
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
How Clean is Your Cloud. Greenpeace Technical Report (2012)
Make IT Green: Cloud Computing and its Contribution to Climate Change. Greenpeace International Technical Report (2010)
Report to Congress on Server and Data Center Energy Efficiency, U.S. Environmental Protection Agency, ENERGY STAR Program (2007)
SMART 2020: Enabling the low carbon economy in the information age, A report by The Climate Group on behalf of the Global eSustainability Initiative (GeSI) (2008)
Where does power go? GreenDataProject (2008), http://www.greendataproject.org
Huff, L.: Berk-Tek: The Choise for Data Center Cabling (2008)
Reddi, V.J., Lee, B.C., Chilimbi, T., Vaid, K.: Mobile processors for energy-efficient web search. ACM Trans. Comput. Syst. 29(3) (2011)
Reddi, V.J., Lee, B.C., Chilimbi, T., Vaid, K.: Web search using mobile cores: quantifying and mitigating the price of efficiency. In: Proceedings of the 37th Annual International Symposium on Computer Architecture (ISCA 2010) (2010)
The SeaMicro SM10000 Family System Overview, Datasheet, SeaMicro Inc. (2011)
Calxeda EnergyCore: ECX-1000 Series, Datasheet, Calxeda, Inc. (2012)
Dean, J., Ghemawat, S.: MapReduce: Simplified Data Processing on Large Clusters. In: 6th Symposium on Operating Systems Design and Implementation (OSDI 2004) (2004)
Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G., Kozyrakis, C.: Evaluating MapReduce for Multi-core and Multiprocessor Systems. In: Proceedings of the 13th Intl. Symposium on High-Performance Computer Architecture (HPCA), Phoenix, AZ (February 2008)
Yoo, R.M., Romano, A., Kozyrakis, C.: Phoenix Rebirth: Scalable MapReduce on a Large-Scale Shared-Memory System. In: Proceedings of the 2009 IEEE International Symposium on Workload Characterization (IISWC), Austin, TX, pp. 198–207 (October 2009)
OMAP4430 Device Silicon Revision, Technical Reference Manual, Texas Instrument
Kaxiras, S., Martonosi, M.: Computer Architecture Techniques for Power-Efficiency. Morgan & Claypool Publishers (2008) 1598292080
Pandaboard, http://www.pandaboard.org
van Eijndhoven, J.: Measuring Power Consumption of the OMAP4430 using the PandaBoard. Vectofabrics Inc. (November 2011)
Powerstat: Power Consumption Calculator for Ubuntu Linux
Bohra, N., Eylon, E.: Micro Servers:An Emerging Category For Data Centers. Server Design Summit (November 2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Kachris, C., Sirakoulis, G., Soudris, D. (2013). Performance Evaluation of Embedded Processor in MapReduce Cloud Computing Applications. In: Yousif, M., Schubert, L. (eds) Cloud Computing. CloudComp 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 112. Springer, Cham. https://doi.org/10.1007/978-3-319-03874-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-03874-2_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03873-5
Online ISBN: 978-3-319-03874-2
eBook Packages: Computer ScienceComputer Science (R0)