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Modeling self-adaptive software systems with learning petri nets

Published: 31 May 2014 Publication History

Abstract

Traditional models have limitation to model adaptive software systems since they build only for fixed requirements, and cannot model the behaviors that change at run-time in response to environmental changes. In this paper, an adaptive Petri net is proposed to model a self-adaptive software system. It is an extension of hybrid Petri nets by embedding a neural network algorithm into them at some special transitions. The proposed net has the following advantages: 1) It can model a runtime environment; 2) The components in the model can collaborate to make adaption decisions; and 3) The computing is done at the local, while the adaption is for the whole system. We illustrate the proposed adaptive Petri net by modeling a manufacturing system.

References

[1]
N. Bencomo, A. Belaggoun, and V. Issarny, Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks. In RAISE’13, pp.7-13, 2013.
[2]
M. Caporuscio, A. Marco, and P. Inverardi, Model-based system reconfiguration for dynamic performance management. Journal of Systems and Software, vol. 80, no.4, pp. 455-473, 2007.
[3]
N. Cardozo, S. González1, K. Mens, R. Straeten, and T. D’Hondt, Modeling and Analyzing Self-Adaptive Systems with Context Petri Nets. In TASE’13, pp.191-198, 2013.
[4]
B. Cheng, R. de Lemos, H. Giese, P. Inverardi, J. Magee, et al., Software engineering for self-adaptive systems: A research roadmap. LNCS, vol.5525, pp.1-26, 2009.
[5]
D. Garlan, S. Cheng, A. Huang, et al., Rainbow: Architecture-Based Self-Adaptation with Reusable Infrastructure. Computer, vol. 37, no. 10, pp. 46-54, 2004.
[6]
C. Ghezzi, L. Pinto, P. Spoletini, and G. Tamburrelli, Managing Non-functional Uncertainty via Model-Driven Adaptivity. In ICSE’13, pp. 33-42, 2013.
[7]
T. Henzinger, P. Ho, and H. Toi, HyTech: A Model Checker for Hybrid Systems. In CAV’97, vol. 1254, pp. 460-463, 1997.
[8]
V. K˚urková, Kolmogorov’s theorem and multilayer neural networks. Neural networks, vol.5, no.3, pp.501-506, 1992.
[9]
R. Nielsen, Theory of the backpropagation neural network. In International Joint Conference on Neural Networks, pp. 593-605, 1989.
[10]
J. Zhang and B. Cheng, Model-Based Development of Dynamically Adaptive Software. In ICSE’06, May 20-28, 2006.

Cited By

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  • (2024)The State of the Art of Emergent Software SystemsIEEE Access10.1109/ACCESS.2024.336990312(31808-31823)Online publication date: 2024
  • (2019)Model-Based Monitoring and Adaptation of Pacemaker Behavior Using Hierarchical Fuzzy Colored Petri-NetsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2018.2861718(1-14)Online publication date: 2019
  • (2017)A Taxonomy and Survey of Cloud Resource Orchestration TechniquesACM Computing Surveys10.1145/305417750:2(1-41)Online publication date: 10-May-2017
  • Show More Cited By

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Published In

cover image ACM Conferences
ICSE Companion 2014: Companion Proceedings of the 36th International Conference on Software Engineering
May 2014
741 pages
ISBN:9781450327688
DOI:10.1145/2591062
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2014

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Author Tags

  1. Requirement modeling
  2. adaptive Petri net
  3. adaptive software system
  4. neural network

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ICSE '14
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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

View all
  • (2024)The State of the Art of Emergent Software SystemsIEEE Access10.1109/ACCESS.2024.336990312(31808-31823)Online publication date: 2024
  • (2019)Model-Based Monitoring and Adaptation of Pacemaker Behavior Using Hierarchical Fuzzy Colored Petri-NetsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2018.2861718(1-14)Online publication date: 2019
  • (2017)A Taxonomy and Survey of Cloud Resource Orchestration TechniquesACM Computing Surveys10.1145/305417750:2(1-41)Online publication date: 10-May-2017
  • (2017)Control Strategies for Self-Adaptive Software SystemsACM Transactions on Autonomous and Adaptive Systems10.1145/302418811:4(1-31)Online publication date: 3-Feb-2017
  • (2016)An information theoretic approach for knowledge representation using Petri nets2016 Future Technologies Conference (FTC)10.1109/FTC.2016.7821606(165-172)Online publication date: Dec-2016
  • (2016)Handling Uncertainty in Self-Adaptive Software Using Self-Learning Fuzzy Neural Network2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC)10.1109/COMPSAC.2016.125(540-545)Online publication date: Jun-2016
  • (2015)Learning Automata-Based Adaptive Petri Net and Its Application to Priority Assignment in Queuing Systems With Unknown ParametersIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2015.240676445:10(1373-1384)Online publication date: Oct-2015
  • (2015)An Improved Exact $\varepsilon$-Constraint and Cut-and-Solve Combined Method for Biobjective Robust Lane ReservationIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2014.236859416:3(1479-1492)Online publication date: Jun-2015
  • (2015)Colored Traveling Salesman ProblemIEEE Transactions on Cybernetics10.1109/TCYB.2014.237191845:11(2390-2401)Online publication date: Nov-2015

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