The thesis proposes a generic, configurable and enhanced goal-oriented decision-making model for self-adaptive software systems. The model has been designed to include feedback control loops as first class entities in the adaptation process whereby the decision-making processes can assess the impact of a previously executed decision, so that better decisions can be made in the future. Furthermore, the model provides the ability to detect and resolve conflicts amongst dependant adaptation requirements. The realization of the decision-model is extremely generic, flexible and extensible. It allows different voting algorithms to be specified for choosing a winner requirement for clusters of flexible adaptation requirements. Moreover, the implementation also allows for the specification of a wide variety of reinforcement learning algorithms to assess the impact of a previously executed decision. The implementation has been developed as a plug-in for a generic Java-based adaptation framework. It was tested using two case studies namely a News Web Application and an IP Telephony System. The aim of the conducted experiments was to assess the impact of the model on the systems goals and to determine the impact of feedback control loops as first class entities in the decision-making process. Based on the obtained results, it can be concluded that the model does improve the overall customer satisfaction level compared to a non-adaptive system. Moreover, it will be concluded that incorporating feedback loops as first class entities yields better results as compared to a decision-making model based solely on policies or goals.
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An Enhanced Goal-Oriented Decision-Making Model for Self-Adaptive Systems
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