The Community for Technology Leaders
RSS Icon
Issue No.03 - May-June (2012 vol.38)
pp: 561-578
A. Kalbasi , Dept. of Electr. & Comput. Eng., Univ. of Calgary, Calgary, AB, Canada
We present a new technique for predicting the resource demand requirements of services implemented by multitier systems. Accurate demand estimates are essential to ensure the efficient provisioning of services in an increasingly service-oriented world. The demand estimation technique proposed in this paper has several advantages compared with regression-based demand estimation techniques, which many practitioners employ today. In contrast to regression, it does not suffer from the problem of multicollinearity, it provides more reliable aggregate resource demand and confidence interval predictions, and it offers a measurement-based validation test. The technique can be used to support system sizing and capacity planning exercises, costing and pricing exercises, and to predict the impact of changes to a service upon different service customers.
service-oriented architecture, multiprocessing systems, regression analysis, capacity planning, DEC, service demand estimation technique, resource demand requirements, multitier systems, service-oriented world, regression-based demand estimation techniques, multicollinearity, system sizing, Benchmark testing, Equations, Software, Mathematical model, Estimation, Frequency modulation, Computers, statistical regression., Benchmarking, resource demand prediction
A. Kalbasi, "DEC: Service Demand Estimation with Confidence", IEEE Transactions on Software Engineering, vol.38, no. 3, pp. 561-578, May-June 2012, doi:10.1109/TSE.2011.23
[1] Amazon Elastic Compute Cloud (Amazon EC2), http://aws. amazon.comec2/, 2011.
[2] C. Amza, A. Chanda, A.L. Cox, S. Elnikety, R. Gil, K. Rajamani, W. Zwaenepoel, E. Cecchet, and J. Marguerite, "Specification and Implementation of Dynamic Web Site Benchmarks," Proc. Fifth IEEE Workshop Workload Characterization, pp. 3-13, Nov. 2002.
[3] Y. Bard and M. Shatzoff, "Statistical Methods in Computer Performance Analysis," Current Trends in Programming Methodology, vol. 3, pp. 1-51, Prentice-Hall, 1978.
[4] G. Casale, E.Z. Zhang, and E. Smirni, "Kpc-Toolbox: Simple Yet Effective Trace Fitting Using Markovian Arrival Processes," Proc. Fifth Conf. Quantitative Evaluation of Systems, pp. 183-187, Sept. 2008.
[5] Y. Dodge and J. Jureckova, Adaptive Regression. Springer, 2000.
[6] N.R. Draper and H. Smith, Applied Regression Analysis. John Wiley & Sons, 1998.
[7] J.J. Dujmovic, "Universal Benchmark Suites," Proc. Seventh Int'l Symp. Modeling, Analysis and Simulation of Computer and Telecomm. Systems, pp. 197-205, 1999.
[8] Eng. Statistics Handbook, handbook /, 2011.
[9] R. Jain, The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. John Wiley & Sons, 1991.
[10] S. Kraft, S. Pacheco-Sanchez, G. Casale, and S. Dawson, "Estimating Service Resource Consumption from Response Time Measurements," Proc. Fourth Int'l Conf. Performance Evaluation Methodologies and Tools, Oct. 2009.
[11] D. Krishnamurthy, "Synthetic Workload Generation for Stress Testing Session-Based Systems," PhD thesis, Dept. of Systems and Computer Eng., Carleton Univ., 2004.
[12] D. Krishnamurthy, J.A. Rolia, and S. Majumdar, "A Synthetic Workload Generation Technique for Stress Testing Session-Based Systems," IEEE Trans. Software Eng., vol. 32, no. 11, pp. 868-882, Nov. 2006.
[13] U. Krishnaswamy and D. Scherson, "A Framework for Computer Performance Evaluation Using Benchmark Sets," IEEE Trans. Computers, vol. 49, no. 12, pp. 1325-1338, Dec. 2000.
[14] T. Kubokawa and M. Srivastava, "Improved Empirical Bayes Ridge Regression Estimators under Multicollinearity," Comm. in Statistics—Theory and Methods, vol. 33, no. 8, pp. 1943-1973, Dec. 2004.
[15] Y. Lu, T. Abdelzaher, C. Lu, L. Sha, and X. Liu, "Feedback Control with Queuing-Theoretic Prediction for Relative Delay Guarantees in Web Servers," Proc. IEEE Real-Time and Embedded Technology and Applications Symp., pp. 208-217, 2003.
[16] D. Menasce, "Computing Missing Service Demand Parameters for Performance Models," Proc. Int'l Computer Measurement Group Conf., pp. 241-248, 2008.
[17] N. Mi, Q. Zhang, A. Riska, E. Smirni, and E. Riedel, "Performance Impacts of Autocorrelated Flows in Multi-Tiered Systems," Performance Evaluation, vol. 64, nos. 9-12, pp. 1082-1101, 2007.
[18] D. Mosberger and T. Jin, "httperf—A Tool for Measuring Web Server Performance," ACM SIGMETRICS Performance Evaluation Rev., vol. 26, no. 3, pp. 31-37, 1998.
[19] J. Neter, M.H. Kutner, C.J. Nachtsheim, and W. Wasserman, Applied Linear Statistical Models. McGraw-Hill, 1996.
[20] G. Pacifici, W. Segmuller, M. Spreitzer, and A. Tantawi, "CPU Demand for Web Serving: Measurement Analysis and Dynamic Estimation," Performance Evaluation, vol. 65, nos. 6/7, pp. 531-553, June 2008.
[21] J. Rolia, A. Kalbasi, D. Krishnamurthy, and S. Dawson, "Resource Demand Modeling for Multi-Tier Services," Proc. First Joint WOSP/SIPEW Int'l Conf. Performance Eng., pp. 207-216, 2010.
[22] J. Rolia and V. Vetland, "Correlating Resource Demand Information with ARM Data for Application Services," Proc. Int'l Workshop Software and Performance, pp. 219-230, 1998.
[23] J. Rolia and V. Vetland, "Parameter Estimation for Performance Models of Distributed Application Systems," Proc. Centre for Advanced Studies on Collaborative Research Conf., pp. 54-63, 1995.
[24] SAP Business by Design, index.epx, 2011.
[25] A.J. Smola and B. Scholkopf, "A Tutorial on Support Vector Regression," Statistics and Computing, vol. 14, no. 3, pp. 199-222, Aug. 2004.
[26] C. Stewart, T. Kelly, and A. Zhang, "Exploiting Nonstationarity for Performance Prediction," ACM SIGOPS Operating Systems Rev., vol. 41, no. 3, pp. 31-44, 2007.
[27] X. Sun, "Estimating Resource Demands for Application Services," MSc thesis, Dept. of Systems and Computer Eng., Carleton Univ., 1999.
[28] TPC-W benchmark,, 2011.
[29] "TPC BENCHMARK W (Web Commerce) Specification," , 2011.
[30] U. Vallamsetty, K. Kant, and P. Mohapatra, "Characterization of E-Commerce Traffic," Electronic Commerce Research, vol. 3, nos. 1/2, pp. 167-192, 2003.
[31] M. Woodside, "The Relationship of Performance Models to Data," Proc. SPEC Int'l Workshop Performance Evaluation: Metrics, Models and Benchmarks, pp. 9-28, 2008.
[32] M. Woodside, T. Zhen, and M. Litoiu, "Service System Resource Management Based on a Tracked Layered Performance Model," Proc. IEEE Int'l Conf. Autonomic Computing, pp. 175-184, 2006.
[33] Q. Zhang, L. Cherkasova, N. Mi, and E. Smirni, "A Regression-Based Analytic Model for Capacity Planning of Multi-Tier Applications," Proc. Fourth IEEE Int'l Conf. Autonomic Computing, June 2007.
20 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool