Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Policy-Centric Integration and Dynamic Composition of Autonomic Computing Techniques
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
A versatile policy toolkit supporting run-time policy reconfiguration
Cluster Computing
An adaptive feedback controller for SIP server memory overload protection
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Self-correlating predictive information tracking for large-scale production systems
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Context-aware device self-configuration using self-organizing maps
Proceedings of the 2011 workshop on Organic computing
| Hi-index | 0.00 |
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.