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• W2182652783 abstract "The realization of decision-making models in self-adaptive systems is still a challenge. Current state-of-the-art self-adaptive systems contain decision-making processes that leverage policy-based, goal-based or architecture-based adaptation models. Policy-based models are well suited to represent mandatory/rigid adaptation requirements. However, they cannot efficiently represent complex or flexible adaptation requirements. Moreover, conflict detection and resolution in these models is non-trivial and extremely error prone. Goal-based models utilize game theory techniques for conflict resolution; choosing a winner requirement from several conflicting requirements using state-of-the-art voting algorithms. This technique only serves systems containing flexible adaptation requirements. Architecturebased models rely predominantly on built-in analytical models, which are not generic. This implies that they cannot be incorporated into other existing or new systems. Furthermore, these models cannot cope with run-time changes that were not accounted for in their formulation, which limits their usage to very specific systems. In this dissertation, we have engineered a generic, configurable and enhanced goaloriented decision-making model that addresses the above-mentioned shortcomings in policybased, goal-based and architecture-based decision-making models. The model provides the ability to represent both mandatory/rigid and flexible requirements. Additionally, the model provides the ability to represent related flexible adaptation requirements as clusters, and allows for the representation of multiple clusters. These clusters enable the execution of multiple corrective actions simultaneously, thus allowing a self-adaptive system to satisfy mandatory/rigid and flexible requirements concurrently. None of the above-mentioned decision-making models have the ability to do so. Additionally, the model has been designed to include feedback control loops as first class entities in the adaptation process. This enables assessing the impact of a previously executed decision, so that better decisions can be made in the future, thus allowing the model to cope with run-time changes. Furthermore, the model provides the ability to detect and resolve conflicts amongst dependant adaptation requirements. The realization of the decision-making 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. Based on the obtained results, it was concluded that the model does improve the overall customer satisfaction level compared to a non-adaptive system. Moreover, it was also concluded that incorporating feedback control loops as first class entities yields better results as compared to a decision-making model based solely on policies or goals." @default.
• W2182652783 created "2016-06-24" @default.
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• W2182652783 date "2011-04-29" @default.
• W2182652783 modified "2023-09-26" @default.
• W2182652783 title "An Enhanced Goal-Oriented Decision-Making Model for Self-Adaptive Systems" @default.
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