Abstract
Cloud datacenters are compute facilities formed by hundreds or even thousands of servers. With the increasing demand of cloud services, energy efficiency of servers in data center has become a significant issue. The knowledge of the energy consumption corresponding to hardware and software configuration is important for operators to optimize energy efficiency of a data center. We are currently working on a predictive model for energy consumption of a server, with inputs as service provided, hardware material equipped (type and quantity of processor, memory and hard drive) and technical environment (energy conversion and cooling). In this article, we characterize some potential factors on the power variation of the servers, such as: original fabrication, position in the rack, voltage variation and temperature of components on motherboard. The results show that certain factors, such as original fabrication, ambient temperature and CPU temperature, have noticeable effects on the power consumption of servers. The experimental results emphasize the importance of adding these external factors into the metric, so as to build an energy predictive model adaptable in real situations.
Similar content being viewed by others
References
Beaty, D. L. (2013). Internal IT load profile variability. ASHRAE Journal, 55(2), 72–74.
Acton, M., Bertoldi, P., Booth, J., Newcombe, L., Rouyer, A., Tozer, R. (2018). Best Practice Guidelines for the EU Code of Conduct on Data Centre Energy Efficiency. In: EUR 29103 EN, Publications Office of the European Union, Luxembourg
Da Costa, G., & Hlavacs, H. (2010). Methodology of measurement for energy consumption of applications. In: 2010 11th IEEE/ACM International Conference on Grid Computing, Brussels (pp. 290–297)
Jarus, M., Oleksiak, A., Piontek, T., & Wȩglarz, J. (2014). Runtime power usage estimation of HPC servers for various classes of real-life applications. Future Generation Computer Systems, 36, 299–310.
Kursun, E., & Cher, C. (2009). Temperature Variation Characterization and thermal management of multicore architectures. IEEE Micro., 29(1), 116–126.
Moss, D., & Bean, J. H. (2009). Energy impact of increased server inlet temperature, APC white paper (vol. 138)
Coles, H. C., Qin, Y., & Price, P. N. (2014). Comparing Server Energy Use and Efficiency Using Small Sample Sizes Coles. In: Ernest Orlando Lawrence Berkeley National Laboratory. Berkeley (No. LBNL-6831E), CA (US)
von Kistowski, J., Block, H., Beckett, J., Spradling, C., Lange, K. D., & Kounev, S. (2016). Variations in cpu power consumption. In: Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering (pp. 147–158). ACM
Orgerie, A. C., Lefevre, L., & Gelas, J. P. (2010). Demystifying energy consumption in grids and clouds. In Green Computing Conference, 2010 International (pp. 335–342) IEEE
W. Torell, K. Brown, and V. Avelar. The Unexpected Impact of Raising Data Center Temperatures, Write paper 221, Revision 0, Schneider Electric Data Center’s Science Center. https://www.schneider-electric.com/en/download/document/APC_VAVR-9SZM5D_EN/
Sampath, S. (2012). Thermal Analysis of High End Servers Based on development of detail model and experiments, Master Degree Report. The university of Texas at Arlington
Patterson, M. K. (2008). The effect of data center temperature on energy efficiency. In 11th Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems, 2008. ITHERM 2008. IEEE. https://doi.org/10.1109/ITHERM.2008.4544393
Haywood, A. M., Sherbeck, J., Phelan, P., Varsamopoulos, G., & Gupta, S. K. S. (2015). The relationship among CPU utilization, temperature, and thermal power for waste heat utilization. Energy Conversion and Management, 95, 297–303.
Bircher, W. L., & John, L. K. (2012). Complete System Power Estimation Using Processor Performance Events. IEEE Transactions on Computers, 61(4), 563–577.
Mair, J., Huang, Z., Eyers, D., & Zhang, H. (2013). Myths in PMC-Based Power Estimation. In J. M. Pierson, G. Da Costa, & L. Dittmann (Eds.), Energy Efficiency in Large Scale Distributed Systems. EE-LSDS 2013. Lecture Notes in Computer Science (vol 8046, pp. 35–50). Berlin, Heidelberg: Springer.
Balouek, D., Carpen Amarie, A., Charrier, G., Desprez, F., Jeannot, E., Jeanvoine, E., et al. (2013). Adding Virtualization Capabilities to the Grid’5000 Testbed. In I. I. Ivanov, M. van Sinderen, F. Leymann, & T. Shan (Eds.), Cloud Computing and Services Science. CLOSER 2012. Communications in Computer and Information Science (vol 367, pp. 3–20). Cham: Springer. https://doi.org/10.1007/978-3-319-04519-1_1
Lange KD, Tricker MG. (2011). The Design and Development of the Server Efficiency Rating Tool (SERT). In: ICPE'11 - Second Joint WOSP/SIPEW International Conference on Performance Engineering, Karlsruhe, Germany, March 14–16, 2011. https://doi.org/10.1145/1958746.1958769
Standard Performance Evaluation Corporation (SPEC). (2014). Power and Performance Benchmark Methodology v2.2. http://www.spec.org/power_ssj2008/docs/SPECpower-Methodology.pdf
Robert, R. (2011). Ubuntu manpage: cpuburn, burnbx, burnk6, burnk7, burnmmx, burnp5, burnp6—a collection. https://patrickmn.com/projects/cpuburn/. Accessed May 28, 2018.
John, M. Stream: Sustainable memory bandwidth in high performance computers. https://www.cs.virginia.edu/stream/. Accessed April 01, 2018.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wang, Y., Nörtershäuser, D., Le Masson, S. et al. Potential effects on server power metering and modeling. Wireless Netw 29, 1077–1084 (2023). https://doi.org/10.1007/s11276-018-1882-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-018-1882-1