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J4  2012, Vol. 46 Issue (4): 725-733    DOI: 10.3785/j.issn.1008-973X.2012.04.022
DVFS-aware CPU service time estimation method
ZHANG Zhen, LI Shan-ping
College of Computer Science and Technology, Zhejiang University, Hangzhou 310029, China
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A service time estimation method that use the product of average frequency and CPU utilization instead of CPU utilization as the dependent variables of regression analysis was proposed in order to mitigate the large error due to the ignorance of CPU dynamic voltage and frequency scaling (DVFS) during existing CPU service time estimation methods. The cpufreq_stats driver of Linux was modified to accurately measure the average CPU frequency, and the problem that the original driver underestimates average frequency was fixed. A method to revise existing frequency readings was proposed for the environment that patching cpufreq_stats is not possible. Experiments with a micro benchmark application in Linux show that DVFS can significantly impact the estimated service time of the classic regression method. For the services with small service time, it can cause around 100% deviation, while the DVFS-aware regression method can still give accurate estimation. Various average frequency measurement approaches were compared. Results show that current tools can not give accurate average frequency, and the relative error can be larger than 40%.

Published: 17 May 2012
CLC:  TP 302.7  
Cite this article:

ZHANG Zhen, LI Shan-ping. DVFS-aware CPU service time estimation method. J4, 2012, 46(4): 725-733.

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[1] ZHANG Q, CHERKASOVA L, MATHEWS G, et al.RCapriccio: a capacity planning and anomaly detection tool for enterprise services with live workloads[C]∥Proceedings of the ACMIFIPUSENIX 2007 International Conference on Middleware. Newport Beach: SpringerVerlag, 2007: 244-265.
[2] ZHANG Q, CHERKASOVA L, MI N, et al.A regressionbased analytic model for capacity planning of multitier applications[J]. Cluster Computing,2008, 11(3): 197-211.
[3] URGAONKAR B, PACIFICI G, SHENOY P, et al. Analytic modeling of multitier Internet applications [J]. ACM Transactions on the Web, 2007,1(1): article 2.
[4] BARHAM P, DONNELLY A, ISAACS R, et al.Using magpie for request extraction and workload modelling[C]∥Proceedings of the 6th Conference on Symposium on Opearting Systems Design and Implementation. San Francisco: USENIX, 2004: 18.
[5] CHEN M Y, KICIMAN E, FRATKIN E, et al. Pinpoint: problem determination in large, dynamic Internet services [C]∥Proceedings of the 2002 International Conference on Dependable Systems and Networks. Los Alamitos: IEEE, 2002: 595-604.
[6] ROLIA J, VETLAND V.Correlating resource demand information with ARM data for application services [C]∥Proceedings of the 1st International Workshop on Software and Performance. Santa Fe: ACM,1998: 219-230.
[7] ZHANG L, XIA C H, SQUILLANTE M S, et al.Workload service requirements analysis: a queueing network optimization approach[C]∥Proceedings of 10th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems. Washington DC: IEEE, 2002: 23-32.
[8] STEWART C, KELLY T, ZHANG A.Exploiting nonstationarity for performance prediction[C]∥Proceedings of the 2nd ACM SIGOPS/EuroSys European Conference on Computer Systems. Lisbon: ACM,2007: 31-44.
[9] CHERKASOVA L, OZONAT K, MI N, et al.Automated anomaly detection and performance modeling of enterprise applications [J]. ACM Transactions on Computer Systems, 2009, 27(3): 1-32.
[10] PACIFICI G, SEGMULLER W, SPREITZER M, et al.CPU demand for web serving: measurement analysis and dynamic estimation[J]. Performance Evaluation, 2008, 65(6/7): 531-553.
[11] MENASCE D A, DOWDY L W, ALMEIDA V A F. Performance by design: computer capacity planning by example[M]. Upper Saddle River: Prentice Hall, 2004: 64-65.
[12] PALLIPADI V, STARIKOVSKIY A.The ondemand governor: past, present and future [C]∥Proceedings of Linux Symposium. Ottawa: \
[s. n.\], 2006: 223-238.
[13] DRAPER N R, SMITH H. Applied regression analysis [M].2nd ed. New York: Wiley, 1981: 417.

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