Evett, Matthew P.

Designing a dependable network for a highly sustainable system gives a challenging network design problem. The network must be highly adaptive to the changes in the network environment. It should also sustain any damages occurring in the network and recover itself quickly and efficiently. This thesis ultimately maps a real network to simulated network by developing a concept of generic nodes and experimentally investigates different parameters that affects the reliability of the system. The work includes designing a simulation for generation of network traffic in a simulated network and studying the behavior of the network with different parameters. The experiment helped us in determining the optimum values of these parameters. For the selected set of experiments and further implies that simulation can determine the nodes different parameter in a control network and will result in a Dependable system.

Genetic Programming is an evolutionary technique for searching through the space of S-expressions for programs that represent optimal or acceptable solutions to a given problem. Genetic Programming often has difficulty in finding the appropriate numeric constants to use in leaf nodes of the S-expressions. This thesis describes the use of local search algorithms to search for numeric constants that will improve the S-expressions found by Genetic Programming. Three methods, Multi-Dimensional Hill Climbing, Vector Hill Climbing, and Numeric Mutation are combined with Genetic Programming to create hybrid systems. The performance of these hybrid systems is analyzed and future directions for improving Genetic Programming with the use of hybrid systems are discussed.

Environmental simulations are computationally very demanding. South Florida Water Management District has implemented the Everglades Landscape Model (ELM) to simulate the ecosystem in South Florida. The current implementation parallelizes all of the model except the canal system. This thesis describes the parallelization of the canal system. We study the canal system and its interaction with the rest of the ELM, and created three distinct parallel implementations. Two of the methods, one-do-all and all-do-all, provide parallelism via task replication while the third method, task-parallel, decomposes the canal system into tasks and uses a locality-based heuristic algorithm to schedule the tasks. We analyze the performance of three methods and discuss future directions for parallelization of the ELM and other environmental models.