Uncertainty handling in goal-driven self-optimization – limiting the negative effect on adaptation

Goal-driven self-optimization through feedback loops has shown effectiveness in reducing oscillating utilities due to a large number of uncertain factors in the runtime environments. However, such self-optimization is less satisfactory when there contains uncertainty in the predefined requirements goal models, such as imprecise contributions and unknown quality preferences, or during the switches of goal solutions, such as lack of understanding about the time for the adaptation actions to take effect. In this paper, we propose to handle such uncertainty in goal-driven self-optimization without interrupting the services. Taking the monitored quality values as the feedback, and the estimated earned value as the global indicator of self-optimization, our approach dynamically updates the quantitative contributions from alternative functionalities to quality requirements, tunes the preferences of relevant quality requirements, and determines a proper timing delay for the last adaptation action to take effect. After applying these runtime measures to limit the negative effect of the uncertainty in goal models and their suggested switches, an experimental study on a real-life online shopping system shows the improvements over goal-driven self-optimization approaches without uncertainty handling

Architectural stability of self-adaptive software systems

This thesis studies the notion of stability in software engineering with the aim of understanding its dimensions, facets and aspects, as well as characterising it. The thesis further investigates the aspect of behavioural stability at the architectural level, as a property concerned with the architecture's capability in maintaining the achievement of expected quality of service and accommodating runtime changes, in order to delay the architecture drifting and phasing-out as a consequence of the continuous unsuccessful provision of quality requirements. The research aims to provide a systematic and methodological support for analysing, modelling, designing and evaluating architectural stability. The novelty of this research is the consideration of stability during runtime operation, by focusing on the stable provision of quality of service without violations. As the runtime dimension is associated with adaptations, the research investigates stability in the context of self-adaptive software architectures, where runtime stability is challenged by the quality of adaptation, which in turn affects the quality of service. The research evaluation focuses on the effectiveness, scale and accuracy in handling runtime dynamics, using the self-adaptive cloud architectures

A framework for robust control of uncertainty in self-adaptive software connectors

Context and motivations. The desired behavior of a system in ubiquitous environments considers not only its correct functionality, but also the satisfaction of its non-functional properties, i.e., its quality of service. Given the heterogeneity and dynamism characterizing the ubiquitous environments and the need for continuous satisfaction of non-functional properties, self-adaptive solutions appear to be an appropriate approach to achieve interoperability. In this work, self-adaptation is adopted to enable software connectors to adapt the interaction protocols run by the connected components to let them communicate in a timely manner and with the required level of quality. However, this self-adaptation should be dependable, reliable and resilient to be adopted in dynamic, unpredictable environments with different sources of uncertainty. The majority of current approaches for the construction of self-adaptive software ignore the uncertainty underlying non-functional requirement verification and adaptation reasoning. Consequently, these approaches jeopardize system reliability and hinder the adoption of self-adaptive software in areas where dependability is of utmost importance. Objective. The main objective of this research is to properly handle the uncertainties in the non-functional requirement verification and the adaptation reasoning part of the self-adaptive feedback control loop of software connectors. This will enable a robust and runtime efficient adaptation in software connectors and make them reliable for usage in uncertain environments. Method. In the context of this thesis, a framework has been developed with the following functionalities: 1) Robust control of uncertainty in runtime requirement verification. The main activity in runtime verification is fine-tuning of the models that are adopted for runtime reasoning. The proposed stochastic approach is able to update the unknown parameters of the models at runtime even in the presence of incomplete and noisy observations. 2) Robust control of uncertainty in adaptation reasoning. A general methodology based on type-2 fuzzy logic has been introduced for the control of adaptation decision-making that adjusts the configuration of component connectors to the appropriate mode. The methodology enables a systematic development of fuzzy logic controllers that can derive the right mode for connectors even in the presence of measurement inaccuracy and adaptation policy conflicts. Results. The proposed model evolution mechanism is empirically evaluated, showing a significant precision of parameter estimation with an acceptable overhead at runtime. In addition, the fuzzy based controller, generated by the methodology, has been shown to be robust against uncertainties in the input data, efficient in terms of runtime overhead even in large-scale knowledge bases and stable in terms of control theory properties. We also demonstrate the applicability of the developed framework in a real-world domain. Thesis statement. We enable reliable and dependable self-adaptations of component connectors in unreliable environments with imperfect monitoring facilities and conflicting user opinions about adaptation policies by developing a framework which comprises: (a) mechanisms for robust model evolution, (b) a method for adaptation reasoning, and (c) tool support that allows an end-to-end application of the developed techniques in real-world domains