We consider autonomic applications to systems for which continuous perfect monitoring of state is not possible. We use Exact-State Observers (ESO) to provide enhanced information about the system state. To achieve optimal configuration of the autonomic controller itself, over a wide range of environmental operating conditions, and across a wide range of unique application domains, we implement a new architecture for dynamic supervision and control systems in which a policy-based autonomic engine automatically selects both its monitoring and actuator components to suit ambient operating conditions.   By using a suite of ESOs tuned for different tradeoffs between real-time responsiveness and extent of system disturbance tolerated, and a policy mechanism to contextually select the most appropriate observer at any given time, we achieve self-configuring and self-optimising behaviours whilst keeping the complexity, resource-requirements and adaptation latency low.
['Richard Anthony', 'Mariusz Pelc', 'Witold Byrski']
Context-aware reconfiguration of autonomic managers in real-time control applications