The Community for Technology Leaders
RSS Icon
Issue No.05 - May (2012 vol.23)
pp: 936-943
Hamzeh Khazaei , University of Manitoba, Winnipeg
Jelena Mišić , Ryerson University, Toronto
Vojislav B. Mišić , Ryerson University, Toronto
Successful development of cloud computing paradigm necessitates accurate performance evaluation of cloud data centers. As exact modeling of cloud centers is not feasible due to the nature of cloud centers and diversity of user requests, we describe a novel approximate analytical model for performance evaluation of cloud server farms and solve it to obtain accurate estimation of the complete probability distribution of the request response time and other important performance indicators. The model allows cloud operators to determine the relationship between the number of servers and input buffer size, on one side, and the performance indicators such as mean number of tasks in the system, blocking probability, and probability that a task will obtain immediate service, on the other.
Cloud computing, performance analysis, response time, queuing theory, semi-Markov process, embedded Markov chain.
Hamzeh Khazaei, Jelena Mišić, Vojislav B. Mišić, "Performance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 5, pp. 936-943, May 2012, doi:10.1109/TPDS.2011.199
[1] L.M. Vaquero, L. Rodero-Merino, J. Caceres, and M. Lindner, "A Break in the Clouds: Towards a Cloud Definition," ACM SIGCOMM Computer Comm. Rev., vol. 39, pp. 50-55, Dec. 2008.
[2] B. Furht, "Cloud Computing Fundamentals," Handbook of Cloud Computing, pp. 3-19, Springer, 2010.
[3] H. Khazaei, J. Mišić, and V.B. Mišić, "Performance Analysis of Cloud Computing Centers," Proc. Seventh Int'l ICST Conf. Heterogeneous Networking for Quality, Reliability, Security and Robustness (QShine), Nov. 2010.
[4] L. Wang, G. von Laszewski, A. Younge, X. He, M. Kunze, J. Tao, and C. Fu, "Cloud Computing: A Perspective Study," New Generation Computing, vol. 28, pp. 137-146, 2010.
[5] L. Kleinrock, Queueing Systems: Theory, vol. 1, Wiley-Interscience, 1975.
[6] Amazon Elastic Compute Cloud, User Guide, API Version ed., Amazon Web Service LLC or Its Affiliate, com/documentationec2, Aug. 2010.
[7] K. Xiong and H. Perros, "Service Performance and Analysis in Cloud Computing," Proc. IEEE World Conf. Services, pp. 693-700, 2009.
[8] J. Baker, C. Bond, J. Corbett, J.J. Furman, A. Khorlin, J. Larsonand, J.M. Leon, Y. Li, A. Lloyd, and V. Yushprakh, "Megastore: Providing Scalable, Highly Available Storage for Interactive Services," Proc. Conf. Innovative Data Systems Research (CIDR), pp. 223-234, Jan. 2011.
[9] B. Yang, F. Tan, Y. Dai, and S. Guo, "Performance Evaluation of Cloud Service Considering Fault Recovery," Proc. First Int'l Conf. Cloud Computing (CloudCom '09), pp. 571-576, Dec. 2009.
[10] B.N.W. Ma and J.W. Mark, "Approximation of the Mean Queue Length of an $M/G/c$ Queueing System," Operations Research, vol. 43, pp. 158-165, 1998.
[11] M. Miyazawa, "Approximation of the Queue-Length Distribution of an $M/GI/s$ Queue by the Basic Equations," J. Applied Probability, vol. 23, pp. 443-458, 1986.
[12] D.D. Yao, "Refining the Diffusion Approximation for the $M/G/m$ Queue," Operations Research, vol. 33, pp. 1266-1277, 1985.
[13] T. Kimura, "A Transform-Free Approximation for the Finite Capacity $M/G/s$ Queue," Operations Research, vol. 44, no. 6, pp. 984-988, 1996.
[14] P. Hokstad, "Approximations for the $M/G/m$ Queues," Operations Research, vol. 26, pp. 510-523, 1978.
[15] H.C. Tijms, "Heuristics for Finite-Buffer Queues," Probability in the Eng. and Informational Sciences, vol. 6, pp. 277-285, 1992.
[16] T. Kimura, "Optimal Buffer Design of an $M/G/s$ Queue with Finite Capacity," Comm. in Statistics Stochastic Models, vol. 12, no. 6, pp. 165-180, 1996.
[17] S.A. Nozaki and S.M. Ross, "Approximations in Finite-Capacity Multi-Server Queues with Poisson Arrivals," J. Applied Probability, vol. 15, pp. 826-834, 1978.
[18] J.M. Smith, "$M/G/c/K$ Blocking Probability Models and System Performance," Performance Evaluation, vol. 52, pp. 237-267, May 2003.
[19] O.J. Boxma, J.W. Cohen, and N. Huffel, "Approximations of the Mean Waiting Time in an $M/G/s$ Queueing System," Operations Research, vol. 27, pp. 1115-1127, 1979.
[20] T. Kimura, "Diffusion Approximation for an $M/G/m$ Queue," Operations Research, vol. 31, pp. 304-321, 1983.
[21] H.C. Tijms, M.H.V. Hoorn, and A. Federgru, "Approximations for the Steady-State Probabilities in the $M/G/c$ Queue," Advances in Applied Probability, vol. 13, pp. 186-206, 1981.
[22] G. Grimmett and D. Stirzaker, Probability and Random Processes, third ed. Oxford Univ. Press, July 2010.
[23] D.P. Heyman and M.J. Sobel, Stochastic Models in Operations Research, vol. 1, Dover, 2004.
[24] H. Takagi, Queueing Analysis: Vacation and Priority Systems, vol. 1, North-Holland, 1991.
[25] K.T. Marshall and R.W. Wolff, "Customer Average and Time Average Queue Lengths and Waiting Times," J. Applied Probability, vol. 8, pp. 535-542, 1971.
[26] D.N. Joanes and C.A. Gill, "Comparing Measures of Sample Skewness and Kurtosis," J. Royal Statistical Soc.: Series D (The Statistician), vol. 47, no. 1, pp. 183-189, 1998.
[27] Maple 13, Maplesoft, Inc., 2009.
[28] RSoft Design, Artifex v.4.4.2, San Jose, CA: RSoft Design Group, Inc., 2003.
48 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool