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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.2 P.165~171

http://doi.org/10.1631/jzus.A0820268


Stochastic individual predicate/transition nets


Author(s):  Yao YUE, Chun-ming ZHANG, Hai-xin WANG, Guo-qiang BAI, Hong-yi CHEN

Affiliation(s):  Institute of Microelectronics, Tsinghua University, Beijing 100084, China

Corresponding email(s):   yue-y05@mails.tsinghua.edu.cn, baigq@tsinghua.edu.cn

Key Words:  Formal model, Parallel system, Performance analysis, Simulation, Stochastic Petri nets


Yao YUE, Chun-ming ZHANG, Hai-xin WANG, Guo-qiang BAI, Hong-yi CHEN. Stochastic individual predicate/transition nets[J]. Journal of Zhejiang University Science A, 2009, 10(2): 165~171.

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author="Yao YUE, Chun-ming ZHANG, Hai-xin WANG, Guo-qiang BAI, Hong-yi CHEN",
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volume="10",
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pages="165~171",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A0820268"
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%A Hai-xin WANG
%A Guo-qiang BAI
%A Hong-yi CHEN
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
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T1 - Stochastic individual predicate/transition nets
A1 - Yao YUE
A1 - Chun-ming ZHANG
A1 - Hai-xin WANG
A1 - Guo-qiang BAI
A1 - Hong-yi CHEN
J0 - Journal of Zhejiang University Science A
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IS - 2
SP - 165
EP - 171
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0820268


Abstract: 
We analyze the drawbacks of generally distributed time transition stochastic Petri nets (GDTT_SPN) in evaluating the performance of parallel systems, and propose a more general model, stochastic individual predicate/transition nets (SIPTN). SIPTN has higher modeling power and could provide more realistic models compared to GDTT_SPN, because in SIPTN the sojourn time distribution is determined not only by the transition, but also by the individuals. It is further proved that GDTT_SPN is a subset of SIPTN. As SIPTN introduces folding techniques from predicate/transition nets, SIPTN models have simpler and more intuitive graphic notations and accordingly higher usability, and thus are suitable for constructing simulation models for parallel systems.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[3] Ajmone Marsan, M., Bobbio, A., Donatelli, S., 1998. Petri nets in performance analysis: an introduction. LNCS, 1491:211-256.

[4] Balbo, G., 2001. Introduction to stochastic Petri nets. LNCS, 2090:117-148.

[5] Genrich, H.J., Lautenbach, K., 1979. The analysis of distributed systems by means of predicate/transition-nets. LNCS, 70:123-146.

[6] Herzog, U., 2002. Formal methods for performance evaluation. LNCS, 2090:1-37.

[7] Jain, J.L., Mohanty, S.G., Böhm, W., 2006. A Course on Queueing Models. Chapman & Hall/CRC, London, New York.

[8] Jensen, K., 1996. Colored Petri Nets: Basic Concepts, Analysis Methods and Practical Use (2nd Ed.). Volume 1, Springer-Verlag Berlin, New York, p.65-85.

[9] Lin, C., Marinescu, D.C., 1988. Stochastic high level Petri nets and applications. IEEE Trans. Comput., 37(7):815-825.

[10] Molloy, M.K., 1981. On the Integration of Delay and Throughput Measures in Distributed Processing Models. PhD Thesis, UCLA, Los Angeles, CA.

[11] Wang, H.X., Yue, Y., Zhang, C.M., Bai, G.Q., Chen, H.Y., 2007. A Novel Unified Control Architecture for a High-performance Network Security Accelerator. Proc. Int. Conf. on Security and Management, p.538-544.

[12] Zimmermann, A., 2007. Stochastic Discrete Event Systems: Modeling, Evaluation, Applications. Springer, Berlin Heidelberg New York, p.65-78.

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