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Control Techniques for Complex Systems Design

Explore the integration of computer science and control techniques to address complex systems design, encompassing transportation, military, and telecom systems. Focus on mathematical algorithms for observation and control.

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Control Techniques for Complex Systems Design

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  1. Combining Computer Science and Control techniques to address complex systems designAlbert Benveniste, IRISA Visiting Committee. March 2004

  2. Design, management, and control, of large distributed infrastructures • Large transportation systems (air traffic control) • Military systems • Telecoms & services: rapid deployment and reconfiguration of new management domains on the top of several existing domains; X-domain management; P2P • Flourishing buzzwords: cooperative control, autonomic computing,… Zooming on mathematical algorithms for the observation and control of such infrastructures

  3. Key fundamental concepts Control Computer science • dynamical systems • models are approximations • optimization & maths • learning & feedback • composition, architecture • model engineering • centralized vs. distributed • dynamicity & autonomy • algorithms for huge, self-evolving, distributed architectures • self-diagnosis, -provisioning, -healing for networks and services

  4. A joint research with Alcatel and France-Telecom Eric Fabre – Bayesian networks, info theory, concurrency Stefan Haar – concurrency theory Claude Jard – formal methods, SW engineering, concurrency Focusing on distributed fault management in networks and services

  5. the diagnosis problem : distributed observation of the hidden state of a dynamical system • Fault propagation – causality • Alarm interleaving – concurrency • Distributed processing diagnoser diagnoser supervision telecommunications network

  6. the diagnosis problem: distributed observation of the hidden state of a dynamical system • Fault propagation – causality • Alarm interleaving – concurrency • Distributed processing diagnoser diagnoser supervision telecommunications network

  7. model-based approach : methodology Montrouge • Structural model (ITU-T…) • physical network topology • network elements • connections model Behavioral model SDH Ring

  8. A typical fault propagation scenario Aubervilliers Montrouge St Ouen Gentilly AU-AIS AU-AIS disabled AU-AIS AU-AIS AU-AIS disabled AU-AIS MS-AIS MS-AIS TF disabled LOS disabled LOS TF

  9. MPLS and SDH domains - impact analysis LSP1.1 C2 LSP1.2 root cause C 2 1 C1 LSP1 3 D LSP2 impacted services B 1 2 LSP2.1 LSP3 4 3 A 2 LSP2.2 3 B3 1 LSP3.1 A1 LSP3.2 B1 A2 B2 MPLS Domain C MPLS Domain B MPLS Domain A

  10. Distributed state inference from alarm observation HOW DO WE GET THE MODEL? Montrouge • asynchronous network of automata • distributed supervisors and sensors • local diagnosis: distributed belief propagation SDH Ring

  11. Self-modeling, and self-deployment of the algorithm Automatic behavioral model generation Behavior of generic NE’s Automatic algorithm generation & deployment Capturing architecture (network discovery) Standards SDH, WDM, OTN, GMPLS

  12. Summary or requirements • Concurrent models: • Local states • Local time – partially ordered by causality • Distributed algorithms robust to asynchronous communications • Self-modeling • Dynamic reconfiguration

  13. Switching to the maths for such problems…

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