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Architecture Differencing for Self-Management: Overview and Implementation

This paper by Alexander Egyed explores how to detect deviations between a system's behavior and expected behavior, focusing on self-awareness, self-healing, self-protection (security), and architectural differencing. It highlights the techniques to simulate a system's model behavior, execute the system, and compare outputs for self-management purposes. The study emphasizes observing real system behavior through probes and translators, simulating software components, and implementing response differencing for self-awareness and self-management benefits. The approach of self-management includes testing, self-healing, and protection, optimizing system behavior, and reconfiguring systems based on models.

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Architecture Differencing for Self-Management: Overview and Implementation

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  1. Architecture Differencing for Self Management Alexander Egyed (Dave Wile and Robert Balzer) Teknowledge Corporation WOSS 2004 October 31, 2004

  2. Overview • How to detect deviation between the system behavior and its expected behavior? • Self awareness • Self Healing • Self Protection (Security) • Architectural Differencing • Execute system • Simulate its model (iff model is behavioral) • Feed simulation with input given to execution • Compare output of simulation and execution

  3. Architecture Differencing

  4. Stimuli/Response From/to user interface, environment, other components System, subsystem, components, classes

  5. Video-On-Demand

  6. Observing the Real System • Probes: • Instrumented source code, mediated interfaces (COTS) • Translators: • Pre-defined mapping table • Generally 1:1 and many:1 mappings (aggregates)

  7. System Reflection Observed system stimuli(after translation) Observed system response(after translation)

  8. Simulating the Real System Concurrent execution and simulation of software components (CD’04 paper)

  9. Outside stimuli Simulating components Parameter for stimuli Progress/state visualization

  10. Real and Simulated Responses

  11. Model Reflection Model stimuli (=observed, translated system stimuli) Model response observed during simulation

  12. Response Differencing

  13. Differencing • given: system input = model input • assume: model and system are consistent • Model state = system state => Compares real response with simulated one If a difference exists then model or system wrong

  14. Differencing + ordering

  15. Self Awareness • System stimuli/responses • Model stimuli/responses • Simulation state reflects system state • Coarse-grained • External observer (avoids weaving into code)

  16. Self Management • Testing (fix system, fix model) • Self Healing (vod, jbi) • Probes: actual system behavior (state) • Simulation: expected system behavior/state • Recovery benefits from knowing about their difference • Self Protection (safe email) • Model and system consistent but system misbehaves! • Self Optimization • Self Reconfiguration => Models suggest self management

  17. Thank you! Alexander Egyed aegyed@teknowledge.com

  18. Final Observations • Purpose • Testing • Self healing, protection, management (model-based) • Models useful during… • development: design, testing • deployment: observe, correct, manage => Level of abstraction • Models describe … • the system • the deployed environment and expectations for self-management

  19. Testing • Generate input (stimuli) and observe the response (output) • Input generated manually or automated • Expected output usually generated manually • Differencing computes the expected output automatically and compares it with the observed output

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