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Issue No.05 - Sept.-Oct. (2012 vol.38)
pp: 1040-1053
Giuliano Casale , Imperial College London, London
Ningfang Mi , Northeastern University, Boston
Ludmila Cherkasova , Hewlett-Packard Laboratories, Palo Alto
Evgenia Smirni , College of William and Mary, Williamsburg
Workloads and resource usage patterns in enterprise applications often show burstiness resulting in large degradation of the perceived user performance. In this paper, we propose a methodology for detecting burstiness symptoms in multi-tier applications but, rather than identifying the root cause of burstiness, we incorporate this information into models for performance prediction. The modeling methodology is based on the index of dispersion of the service process at a server, which is inferred by observing the number of completions within the concatenated busy times of that server. The index of dispersion is used to derive a Markov-modulated process that captures burstiness and variability of the service process at each resource well and that allows us to define queueing network models for performance prediction. Experimental results and performance model predictions are in excellent agreement and argue for the effectiveness of the proposed methodology under both bursty and nonbursty workloads. Furthermore, we show that the methodology extends to modeling flash crowds that create burstiness in the stream of requests incoming to the application.
index of dispersion, Capacity planning, multi-tier applications, bursty workload, bottleneck switch
Giuliano Casale, Ningfang Mi, Ludmila Cherkasova, Evgenia Smirni, "Dealing with Burstiness in Multi-Tier Applications: Models and Their Parameterization", IEEE Transactions on Software Engineering, vol.38, no. 5, pp. 1040-1053, Sept.-Oct. 2012, doi:10.1109/TSE.2011.87
[1] G. Banga and P. Druschel, "Measuring the Capacity of a Web Server under Realistic Loads," World Wide Web, vol. 2, nos. 1/2, pp. 69-83, 1999.
[2] G. Bolch, S. Greiner, H. de Meer, and K.S. Trivedi, Queuing Networks and Markov Chains. John Wiley and Sons, 2006.
[3] G. Casale, N. Mi, and E. Smirni, "Model-Driven System Capacity Planning under Workload Burstiness," IEEE Trans. Computers, vol. 59, no. 1, pp. 66-80, Jan. 2010.
[4] D. Garcia and J. Garcia, "TPC-W E-Commerce Benchmark Evaluation," Computer, vol. 36, pp. 42-48, Feb. 2003.
[5] R. Gusella, "Characterizing the Variability of Arrival Processes with Indexes of Dispersion," IEEE J. Selected Areas Comm., vol. 19, no. 2, pp. 203-211, Feb. 1991.
[6] A. Heindl, "Correlation Bounds for Second-Order MAPs with Application to Queuing Network Decomposition," Performance Evaluation, vol. 63, no. 6, pp. 553-577, June 2006.
[7] , 2012.
[8] A. Horváth and G. Horváth, and M. Telek, "A Joint Moments Based Analysis of Networks of MAP/MAP/1 Queues," Performance Evaluation, vol. 67, no. 9, pp. 759-778, 2010.
[9] G. Latouche and V. Ramaswami, Introduction to Matrix Analytic Methods in Stochastic Modeling. SIAM, 1999.
[10] E.D. Lazowska, J. Zahorjan, G.S. Graham, and K.C. Sevcik, Quantitative System Performance. Prentice-Hall, 1984.
[11] S. Li and C. Hwang, "Queue Response to Input Correlation Functions: Discrete Spectral Analysis," IEEE/ACM Trans. Networking, vol. 1, no. 6, pp. 678-692, Dec. 1993.
[12] Z. Liu, N. Niclausse, and C. Jalpa-Villanueva, "Traffic Model and Performance Evaluation of Web Servers," Performance Evaluation, vol. 46, nos. 2/3, pp. 77-100, 2001.
[13] Z. Liu, L. Wynter, C.H. Xia, and F. Zhang, "Parameter Inference of Queuing Models for IT Systems Using End-to-End Measurements," Performance Evaluation, vol. 63, no. 1, pp. 36-60, 2006.
[14] D.A. Menascé and V.A.F. Almeida, Scaling for E-Business: Technologies, Models, Performance, and Capacity Planning. Prentice-Hall, 2000.
[15] N. Mi, G. Casale, L. Cherkasova, and E. Smirni, "Burstiness in Multi-Tier Applications: Symptoms, Causes, and New Models," Proc. Ninth ACM/IFIP/USENIX Int'l Conf. Middleware, pp. 265-286, Dec. 2008.
[16] N. Mi, G. Casale, L. Cherkasova, and E. Smirni, "Sizing Multi-Tier Systems with Temporal Dependence: Benchmarks and Analytic Models," J. Internet Services and Applications, vol. 1, no. 2, pp. 117-134, Aug. 2010.
[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] S. Ranjan, J. Rolia, H. Fu, and E. Knightly, "QoS-Driven Server Migration for Internet Data Centers," Proc. IEEE Int'l Workshop Quality of Service, 2002.
[19] J. Rolia and V. Vetland, "Correlating Resource Demand Information with Arm Data for Application Services," Proc. First Int'l Workshop Software and Performance, pp. 219-230, 1998.
[20] R. Sadre and B.R. Haverkort, "Flows in Networks of MAP/MAP/1 Queues," Proc. GI/ITG Conf. on Measuring, Modelling and Evaluation of Computer and Comm. Systems, pp. 195-208, 1998.
[21] B. Urgaonkar, G. Pacifici, P. Shenoy, M. Spreitzer, and A. Tantawi, "An Analytical Model for Multi-Tier Internet Services and Its Applications," Proc. ACM SIGMETRICS Int'l Conf. Measurement and Modeling of Computer Systems, pp. 291-302, June 2005.
[22] B. Urgaonkar, P. Shenoy, A. Chandra, and P. Goyal, "Dynamic Provisioning of Multi-Tier Internet Applications," Proc. Second Int'l Conf. Autonomic Computing, pp. 217-228, 2005.
[23] D. Villela, P. Pradhan, and D. Rubenstein, "Provisioning Servers in the Application Tier for E-Commerce Systems," ACM Trans. Internet Technology, vol. 7, no. 1, p. 7, 2007.
[24] Q. Zhang, L. Cherkasova, G. Mathews, W. Greene, and E. Smirni, "R-Capriccio: A Capacity Planning and Anomaly Detection Tool for Enterprise Services with Live Workloads," Proc. ACM/IFIP/USENIX Int'l Conf. Middleware, pp. 244-265, 2007.
[25] Q. Zhang, L. Cherkasova, and E. Smirni, "A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications," Proc. Fourth Int'l Conf. Autonomic Computing, p. 27, 2007.
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