Context-aware real-time systems with autonomie controllers

This paper considers autonomic applications to systems for which continuous perfect monitoring of state is not possible. We use sophisticated exact-state observers to provide enhanced information about the system state. In order that we can 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 component (a state observer) and its actuator component to suit ambient operating conditions. We show how the hybrid approach enables autonomic computing to be deployed in systems in which the target system cannot be monitored continually and perfectly. We also show how the observer window size can be dynamically selected to give the best controller performance for any level of system disturbance.