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Bio-Design and Manufacturing  2022 Vol.5 No.1 P.31~39

10.1631/jzus.2004.0031


Using multi-class queuing network to solve performance models of e-business sites


Author(s):  ZHENG Xiao-ying, CHEN De-ren

Affiliation(s):  College of Computer Science, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zhengbetty@hotmail.com, drchen@zju.edu.cn

Key Words:  Queuing network (QN), Multi-class, Performance, E-business


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ZHENG Xiao-ying, CHEN De-ren. Using multi-class queuing network to solve performance models of e-business sites[J]. Journal of Zhejiang University Science D, 2022, 5(1): 31~39.

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Abstract: 
Due to e-business's variety of customers with different navigational patterns and demands, multi-class queuing network is a natural performance model for it. The open multi-class queuing network (QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

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Reference

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[3] Kleinrock, L., 1975. Queuing Systems. Vol. I: Theory, Wiley, New York, p.69-97.

[4] Lazowska, E. D., Zahorjan, J., Graham, G.. S. and Sevcik, K. C., 1984. Quantitative System Performance: Computer System Analysis Using Queuing Network Models. Prentice-Hall, Englewood Cliffs, New Jersey 07632, p.127-151.

[5] Little, J., 1961. A proof of the Queuing Formula L = W. Operations Research, 9:383-387.

[6] Menasce, D. A. and Almeida, V. A. F., 1998. Capacity Planning for Web Performance: Metrics, Models and Methods. Prentice Hall, Upper Saddle River, NJ, p.197-220.

[7] Menasce, D. A. and Almeida, V. A. F., 2000. Scaling for E-Business Technologies, Models, Performance, and Capacity Planning. Prentice Hall, Upper Saddle River, NJ, p.223-374.

[8] Reiser, M. and Lavenberg, S., 1980. Mean-value analysis of closed multi-chain queuing networks. J. ACM, 27(2):313-323.

[9] Zhong, Y.S., Chen, D.R. and Shi, M.H., 2002. Estimation of financial loss ratio for E-insurance: a quantitative model. Journal of Zhejiang University SCIENCE, 3(2):140-147.

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