Modeling Self-Adaptive Software Systems With Learning Petri Nets

Traditional models unable to model adaptive software systems since they deal with fixed requirements only, but cannot handle the behaviors that change at runtime in response to environmental changes. In this paper, an adaptive Petri net (APN) 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 while the system is running; and 3) the computation is done at the local component, while the adaption is for the whole system. We illustrate the proposed APN by modeling a manufacturing system.

[1]  Kotaro Hirasawa,et al.  Learning Petri network and its application to nonlinear system control , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Tobias Schüle,et al.  Verifying the adaptation behavior of embedded systems , 2006, SEAMS '06.

[3]  MengChu Zhou,et al.  One-Step Look-Ahead Maximally Permissive Deadlock Control of AMS by Using Petri Nets , 2013, TECS.

[4]  Jeffrey J. P. Tsai,et al.  An empirical evaluation of deadlock detection in software architecture specifications , 1999, Ann. Softw. Eng..

[5]  Irvin R. Jones,et al.  Synthesis of intelligent switching systems using neural Petri nets , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[6]  Paola Inverardi,et al.  Model-based system reconfiguration for dynamic performance management , 2007, J. Syst. Softw..

[7]  D. Zaitsev,et al.  State equations and equivalent transformations for timed petri nets , 1997 .

[8]  MengChu Zhou,et al.  Modeling, analysis, simulation, scheduling, and control of semiconductor manufacturing systems: A Petri net approach , 1998 .

[9]  Valérie Issarny,et al.  Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks , 2013, 2013 2nd International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE).

[10]  Ladan Tahvildari,et al.  Self-adaptive software: Landscape and research challenges , 2009, TAAS.

[11]  Betty H. C. Cheng,et al.  Model-based development of dynamically adaptive software , 2006, ICSE.

[12]  David Garlan,et al.  Rainbow: architecture-based self-adaptation with reusable infrastructure , 2004 .

[13]  Giovanni Denaro,et al.  Towards self-adaptive service-oriented architectures , 2006, TAV-WEB '06.

[14]  Mary Shaw,et al.  Software Engineering for Self-Adaptive Systems: A Research Roadmap , 2009, Software Engineering for Self-Adaptive Systems.

[15]  Mary Shaw,et al.  Engineering Self-Adaptive Systems through Feedback Loops , 2009, Software Engineering for Self-Adaptive Systems.

[16]  Vera Kurková,et al.  Kolmogorov's theorem and multilayer neural networks , 1992, Neural Networks.

[17]  Kenneth L. McMillan,et al.  Symbolic model checking , 1992 .

[18]  Jan Lunze,et al.  Hybrid modelling of continuous-variable systems with application to supervisory control , 1997, 1997 European Control Conference (ECC).

[19]  Seyed Masoud Sadjadi,et al.  Composing adaptive software , 2004, Computer.

[21]  Hecht-Nielsen.  Theory of the backpropagation neural network , 1989 .

[22]  MengChu Zhou,et al.  Resource-Oriented Petri Net for Deadlock Avoidance in Flexible Assembly Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  Bernard Widrow,et al.  Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[24]  Danny Weyns,et al.  A survey of formal methods in self-adaptive systems , 2012, C3S2E '12.

[25]  Richard N. Taylor,et al.  Policy-based self-adaptive architectures: a feasibility study in the robotics domain , 2008, SEAMS '08.

[26]  L. C. Bueno,et al.  A Reference Architecture for Component-Based Self-Adaptive Software Systems , 2012 .

[27]  Thomas A. Henzinger,et al.  HYTECH: a model checker for hybrid systems , 1997, International Journal on Software Tools for Technology Transfer.

[28]  A. Prasad Sistla,et al.  SMC: A Symmetry Based Model Checker for Verification of Liveness Properties , 1997, CAV.

[29]  Pablo Chacin,et al.  Collectives: a framework for self-adaptive P2P applications , 2007, ARM '07.

[30]  MengChu Zhou,et al.  A parameterized liveness and ratio-enforcing supervisor for a class of generalized Petri nets , 2013, Autom..

[31]  MengChu Zhou,et al.  Hybrid Petri Net Modeling and Schedulability Analysis of High Fusion Point Oil Transportation Under Tank Grouping Strategy for Crude Oil Operations in Refinery , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[32]  Raymond A. DeCarlo,et al.  Analysis of a hybrid system using symbolic dynamics and Petri Nets , 1994, Autom..

[33]  Sol M. Shatz,et al.  Design and Implementation of a Petri Net Based Toolkit for Ada Tasking Analysis , 1990, IEEE Trans. Parallel Distributed Syst..

[34]  René David,et al.  On Hybrid Petri Nets , 2001, Discret. Event Dyn. Syst..

[35]  MengChu Zhou,et al.  Robust control reconfiguration of resource allocation systems with Petri nets and integer programming , 2014, Autom..

[36]  Carlo Ghezzi,et al.  Managing non-functional uncertainty via model-driven adaptivity , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[37]  D.S. Loubach,et al.  Self-adaptive component-based interoperability framework for real-time systems , 2007, 2007 IEEE/AIAA 26th Digital Avionics Systems Conference.

[38]  Peyman Oreizy,et al.  An architecture-based approach to self-adaptive software , 1999, IEEE Intell. Syst..

[39]  Kim Mens,et al.  Modeling and Analyzing Self-Adaptive Systems with Context Petri Nets , 2013, 2013 International Symposium on Theoretical Aspects of Software Engineering.

[40]  MengChu Zhou,et al.  Modeling, Analysis and Control of Dual-Arm Cluster Tools With Residency Time Constraint and Activity Time Variation Based on Petri Nets , 2012, IEEE Transactions on Automation Science and Engineering.

[41]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[42]  MengChu Zhou,et al.  Petri Net Modeling and Cycle-Time Analysis of Dual-Arm Cluster Tools With Wafer Revisiting , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.