Model-Based Engineering for Certification of Complex Adaptive Network Systems

Certifying a network system S involves the assessment of how good S meets its intended QoS objectives in a backdrop of uncontrolled external environment conditions incident on S. For complex network systems where a reasonably accurate and tractable computational model of S may not be known, hierarchical approaches based on cyber-physical systems (CPS) principles are attractive. Here, a piece-wise linearized simple model of S allows a controller to drive S with test inputs and evaluate the output behavior of S over a limited operating region. With model plug-in and controller algorithm switching, a management entity reasons about the behavior of S under different environment conditions and test inputs, to certify S with high confidence. The paper presents a case study of multi-source video congestion control over a bandwidth-limited network path to illustrate our CPS-based certification method.

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

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

[3]  Leandros Tassiulas,et al.  Optimization based rate control for multirate multicast sessions , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[4]  Kang-Won Lee,et al.  A comparison of two popular end-to-end congestion control algorithms: the case of AIMD and AIPD , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

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

[6]  Marcel Staroswiecki,et al.  A Comparative Analysis of AI and Control Theory Approaches to Model-based Diagnosis , 2000, ECAI.

[7]  Kang-Won Lee,et al.  A Comparison of End-to-End Congestion Control Algorithms : The Case of AIMD and AIPD , 2001 .

[8]  Gail E. Kaiser,et al.  Self-managing systems: a control theory foundation , 2005, 12th IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS'05).

[9]  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.

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

[11]  Jun Wu,et al.  Distributed adaptation algorithms for rate-controlled video multicast over shared infrastructure networks , 2010, 2010 Second International Conference on COMmunication Systems and NETworks (COMSNETS 2010).

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

[13]  Anthony Rowe,et al.  A Model-Based Design Approach for Wireless Sensor-Actuator Networks , 2010 .

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

[15]  George R. Ribeiro-Justo,et al.  Process/sup NFL/: a language for describing non-functional properties , 2002, Proceedings of the 35th Annual Hawaii International Conference on System Sciences.

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

[17]  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.