Discrete-Event systems (DES) is a framework in which problems are modelled as finite-state automata and a solution in the form of a supervisory control scheme can be automatically synthesized via an exhaustive search through the state space of the system. Various extensions to the standard DES framework have been introduced to allow it to be applied to a greater variety of problems. When the system in question is very large or varies with time, a limited lookahead policy can be adopted, in which control decisions are made on-the-fly by looking at finite-step projections of the behaviour of the system's underlying automata. This work presents a new approach to limited lookahead supervision which incorporates many of the extensions to DES that are already present in the literature, such as event probability and string desirability. When dealing with a limited lookahead technique, the projected system behaviour is represented as a lookahead tree with some depth limit decided on by the user. It can be difficult to strike a balance between the complexities associated with storing and analyzing the trees and the amount of information available to make decisions, both of which increase with depth. This work also presents a set of methods which are designed to aid in accurately estimating the state space of lookahead trees with the intent of simplifying the process of determining a favourable depth to use. Finally, the approaches introduced herein are applied to a simulation of an infectious disease outbreak, primarily to showcase them in action, but also for the possibility of illuminating any useful information for real-world health units. / Thesis (Master, Computing) -- Queen's University, 2012-01-20 19:35:58.007

Large-scale distributed computing systems such as data centers are hosted on heterogeneous and networked servers that execute in a dynamic and uncertain operating environment, caused by factors such as time-varying user workload and various failures. Therefore, achieving stringent quality-of-service goals is a challenging task, requiring a comprehensive approach to performance control, fault diagnosis, and failure recovery. This work presents a model-based approach for fault management, which integrates limited lookahead control (LLC), diagnosis, and fault-tolerance concepts that: (1) enables systems to adapt to environment variations, (2) maintains the availability and reliability of the system, (3) facilitates system recovery from failures. We focused on memory leak errors in this thesis. A characterization function is designed to detect memory leaks. Then, a LLC is applied to enable the computing system to adapt efficiently to variations in the workload, and to enable the system recover from memory leaks and maintain functionality.