Cyber-Physical Systems Based Modeling of Dependability of Complex Network Systems

We use the cyber-physical systems (CPS) framework to infuse intelligent adaptation behaviors in complex network systems to achieve dependability. The CPS framework is anchored on "intelligent physical worlds" (IPW) around which complex adaptation behaviors are built. An IPW is an embodiment of control software functions wrapped around the raw physical processes (e.g., servers, links, sensors, actuators), performing the core system activities while adapting its behavior to the changing environment conditions and user inputs. The IPW exhibits an intelligent behavior over a limited operating region of the system (in contrast with traditional models where the physical world is dumb). To perform over wide operating regions, the IPW interacts with an intelligent computational world (ICW) to patch itself with control parameters and procedures relevant in those changed conditions. The modular decomposition of an application into IPW and ICW lowers the design complexity of dependable network systems, and simplifies the system verification/testing.

[1]  Sang Hyuk Son,et al.  Feedback Control Architecture and Design Methodology for Service Delay Guarantees in Web Servers , 2006, IEEE Transactions on Parallel and Distributed Systems.

[2]  David H. Ackley,et al.  Building diverse computer systems , 1997, Proceedings. The Sixth Workshop on Hot Topics in Operating Systems (Cat. No.97TB100133).

[3]  Kaliappa Nadar Ravindran,et al.  Replica Voting: a Distributed Middleware Service for Real-time Dependable Systems , 2006, 2006 1st International Conference on Communication Systems Software & Middleware.

[4]  Russell C. Eberhart,et al.  Computational intelligence - concepts to implementations , 2007 .

[5]  Yuhui Shi,et al.  chapter two – Computational intelligence , 2007 .

[6]  Marcus Brunner,et al.  Probabilistic decentralized network management , 2009, 2009 IFIP/IEEE International Symposium on Integrated Network Management.

[7]  A. Polychronopoulos,et al.  Multiple sensor collision avoidance system for automotive applications using an IMM approach for obstacle tracking , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[8]  Klara Nahrstedt,et al.  A control-based middleware framework for quality-of-service adaptations , 1999, IEEE J. Sel. Areas Commun..

[9]  Marco Zennaro,et al.  CSL: A Language to Specify and Re-specify Mobile Sensor Network Behaviors , 2009, 2009 15th IEEE Real-Time and Embedded Technology and Applications Symposium.

[10]  S. Bush Complexity and Vulnerability Analysis (Extended Abstract) , 2003 .

[11]  Arnd Poetzsch-Heffter,et al.  Slicing for model reduction in adaptive embedded systems development , 2008, SEAMS '08.

[12]  Chan-Gun Lee,et al.  Incorporating Resource Safety Verification to Executable Model-based Development for Embedded Systems , 2008, 2008 IEEE Real-Time and Embedded Technology and Applications Symposium.

[13]  Kaliappa Nadar Ravindran Managing Robustness of Distributed Applications Under Uncertainties: An Information Assurance Perspective , 2011, CSIIRW '10.

[14]  Priya Narasimhan,et al.  Experiences with a CANoe-based fault injection framework for AUTOSAR , 2010, 2010 IEEE/IFIP International Conference on Dependable Systems & Networks (DSN).

[15]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[16]  穂鷹 良介 Non-Linear Programming の計算法について , 1963 .

[17]  Jean-Charles Fabre,et al.  Adaptive fault tolerant systems: reflective design and validation , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[18]  Peng Liu,et al.  Incentive-based modeling and inference of attacker intent, objectives, and strategies , 2003, CCS '03.