A Model Integrated Framework for Designing and Optimization of Self-managing Computing Systems
This thesis addresses the problem of managing computing systems using an integration of model-based control techniques and efficient AI search strategies. The proposed control approach uses the system model to forecast all future system behavior up to a certain horizon and then searches for the best path for the system based on a given utility function. In practical computing systems, however, the large number of control (tuning) options directly affects the computational overhead of the control module which executes in the background at run-time, and ultimately slows down the overall system. To handle this problem, several search algorithms are introduced to improve the controller's performance. This thesis also presents a model integrated framework, referred to as the Automatic Control Modeling Environment (ACME), to facilitate the use of control-based technology for self-management in computation systems. Control-theoretic concepts like above have been investigated and applied successfully to automate the management of computation systems of the control technology. ACME is a domain-specific graphical modeling environment with automated synthesis tools. The framework allows domain engineers to develop models for general computation systems and to capture their performance requirements and operational constraints. The framework can automatically generates executable codes for the controllers based on the given system model and specifications. A case study of an online processor power management is used to demonstrate the effectiveness of the new search techniques for the model-based control approach as well as the application of the ACME.