| Summary: | The aim of this thesis is to propose an integrated generic intelligent decision-making framework that can be employed in the design and construction of computing infrastructures where high flexibility and dynamism are essential. In fact, the main problem with many decision making systems is that they are designed for specific purposes which make them unsuitable to be deployed in a complex system, where the level of unknown / uncertainties is high. The proposed framework is generic enough to address this limitation as it could be deployed across different computing architectures or systems, or redeployed to serve a particular purpose. The research study starts with the proposal of two theoretical concepts as part of an intelligent information management approach for a new integrated intelligent decision-making framework. The first concentrates on the thinking and learning processes to achieve the best effort decision via logical reasoning strategies. It determines the best execution path under particular circumstances in a given computing environment. The second concept focuses on data capturing techniques using distributed sensing devices which act as sensors for a decision-making unit (i.e. an input / output (IO) interface for thinking and learning processes). A model to describe perceived sensory perception is proposed, as well as an observation technique to monitor the proposed model. These concepts are then translated into an intelligent decision-making framework, which is capable of interpreting and manipulating available information to offer the best effort solution based on available resources, rather than relying heavily on additional powerful physical resources to provide a precise solution. Therefore, the accuracy and precision of decision-making depends on the applied logic and learning processes. Indirectly, this framework attempts to solve integration problems related to the aspect of "Intelligent" into practical day-to-day problem solving applications. A working prototype based on the proposed framework was developed and presented for an evaluation, to verify the framework competence in operating with computing infrastructure, and whether it is capable of making sensible decisions upon request, and whether it is able to learn from its decisions via the received feedback. To achieve this, the behaviour of the prototype is accessed against the growth in the number of experiences and amount of knowledge collected during the execution process. Finally, it has concluded that proposed concepts and framework operates well in term of decision-making capabilities and reasoning strategies.
|
|---|
