Abstract
We introduce an approach to energy efficiency policies evaluation in various application fields, based on widely available technical and software tools. The approach is based on a simple client-server-type application run on a single linux-operated laptop, equipped with standard frequency scaling tools. We validate the approach by analyzing two energy efficiency policies: the celebrated hysteretic control and the randomized switching (introduced recently) in a single-server queue. Explicit analytical results are obtained by means of Matrix-Analytic method, and a simulation model based on discrete event stochastic simulation (a particular case of the generalized Kiefer–Wolfowitz stochastic recursion introduced recently) allows to obtain performance and energy estimates, when analytical results are inappropriate. The results of real-world experiments are introduced, and applicability of the approach is discussed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bekker, R.: Queues with state-dependent rates. Ph.D. thesis, Technische Universiteit Eindhoven, Eindhoven (2005)
Do, T.V., Krieger, U.R., Chakka, R.: Performance modeling of an Apache Web server with a dynamic pool of service processes. Telecommun. Syst. 39(2), 117–129 (2008). http://link.springer.com/10.1007/s11235-008-9116-y
Gandhi, A., Harchol-Balter, M., Das, R., Kephart, J.O., Lefurgy, C.: Power capping via forced idleness. In: Proceedings of Workshop on Energy Efficient Design, pp. 1–6 (2009). http://repository.cmu.edu/compsci/868/
Gandhi, A., Harchol-Balter, M., Das, R., Lefurgy, C.: Optimal power allocation in server farms. In: Proceedings of the Eleventh International Joint Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2009, pp. 157–168. ACM, New York (2009). https://doi.org/10.1145/1555349.1555368
Gebrehiwot, M.E., Aalto, S., Lassila, P.: Energy efficient load balancing in web server clusters. In: 2017 29th International Teletraffic Congress (ITC 29), vol. 3, pp. 13–18, September 2017
Gebrehiwot, M.E., Aalto, S.A., Lassila, P.: Optimal sleep-state control of energy-aware m/g/1 queues. In: Proceedings of the 8th International Conference on Performance Evaluation Methodologies and Tools, VALUETOOLS 2014, pp. 82–89. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels (2014). https://doi.org/10.4108/icst.Valuetools.2014.258149
Hanappe, P.: Fine-grained CPU throttling to reduce the energy footprint of volunteer computing. Technical report, Sony Computer Science Laboratory Paris (2012). http://low-energy-boinc.cslparis.fr/info/images/f/fd/Hanappe-12a.pdf
He, Q.M.: Fundamentals of Matrix-Analytic Methods. Springer, New York (2014)
Hopper, J.: Reduce linux power consumption, part 1: The cpufreq subsystem (2009). https://www.ibm.com/developerworks/library/l-cpufreq-1/
Kecskemeti, G., Hajji, W., Tso, F.P.: Modelling low power compute clusters for cloud simulation. In: 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), pp. 39–45, March 2017
von Kistowski, J., Schreck, M., Kounev, S.: Predicting power consumption in virtualized environments. In: Fiems, D., Paolieri, M., Platis, A.N. (eds.) EPEW 2016. LNCS, vol. 9951, pp. 79–93. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46433-6_6
Morozov, E., Rumyantsev, A.: A state-dependent control for green computing. In: Abdelrahman, O.H., Gelenbe, E., Gorbil, G., Lent, R. (eds.) Information Sciences and Systems 2015. LNEE, vol. 363, pp. 57–67. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-22635-4_5
Morozov, E., Rumyantsev, A., Kalinina, K.: Inequalities for workload process in queues with NBU/NWU input. In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds.) ICMMI 2017. AISC, vol. 659, pp. 535–544. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-67792-7_52
Murthy, G.R., Rumyantsev, A.: On an exact solution of the rate matrix of quasi-birth-death process with small number of phases. In: Proceedings: 31st European Conference on Modelling and Simulation ECMS 2017, Budapest, Hungary, pp. 713–719, 23–26 May 2017. https://doi.org/10.7148/2017-0713, oCLC: 993291446
Neuts, M.F.: Matrix-Geometric Solutions in Stochastic Models. Johns Hopkins University Press, Baltimore (1981)
Pallipadi, V.: Enhanced intel speedstep technology and demand-based switching on linux (2010). https://software.intel.com/en-us/articles/enhanced-intel-speedstepr-technology-and-demand-based-switching-on-linux/
R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2017). https://www.R-project.org/
Rumyantsev, A., Kalinina, K., Morozova, T.: Stochastic modelling of the supercomputer with threshold-based service rate control. In: Distributed Computer and Communication Networks: Control, Computation, Communications: Proceedings of the 20 International Scientific Conference, Moscow, pp. 286–290, 25–29 September 2017. (in Russian)
Acknowledgements
Authors thank the Editor and anonymous referees for reviewing the paper and providing some helpful comments. Authors thank Dr. Rostislav Razumchik for some very useful comments. The research is supported by RF President’s Grant No. MK-1641.2017.1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Rumyantsev, A., Zueva, P., Kalinina, K., Golovin, A. (2018). Evaluating a Single-Server Queue with Asynchronous Speed Scaling. In: German, R., Hielscher, KS., Krieger, U. (eds) Measurement, Modelling and Evaluation of Computing Systems. MMB 2018. Lecture Notes in Computer Science(), vol 10740. Springer, Cham. https://doi.org/10.1007/978-3-319-74947-1_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-74947-1_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74946-4
Online ISBN: 978-3-319-74947-1
eBook Packages: Computer ScienceComputer Science (R0)