<p>This thesis investigates the adaptation to climate change that is taking place in the WaterSupply and Waste Water (WSW) sector of the Stockholm Region. The adaptation processis analyzed in terms of building adaptive capacity and implementing adaptive decisions.Theories on organizational learning and the concept of an organization’s adaptation spaceare used to understand the factors that influence the adaptation process and the capacityof the studied WSW organizations to adapt to climate change. A case study approach hasbeen used and by focusing the research on four regional WSW organizations – thatcomprise a majority of the region’s WSW activities – it is argued that conclusionsrelevant to the region’s WSW sector as a whole can be made. Semi-structured interviewswith the complete management board – in three out of four organizations – and officialdocuments and reports, are the main sources of primary data for the analysis.The results show that adaptation to climate change is occuring in the WSW sector of theStockholm Region. The adaptation is mainly taking the form of building adaptivecapacity and there is only limited evidence of implementation of adaptive decisions. Theresearch suggests that there are few technical and organizational limitations foradaptation to take place and that the main factors influencing the adaptation space of thesector is how the climate change issue and risks are interpreted and perceptions of howthe WSW organizations should function and use their limited economical resources. Twoconclusions are drawn from these results: first, factors influencing the feasibility andattractiveness of different adaptation options need to be included and analysed in order tounderstand the actual adaptation space of an organization. Second, due to the factorsidentified as influencing the adaptation space, it is unlikely, at present, that robustinfrastructure solutions – which have been suggested in the literature as a viable way todeal with the intrinsic uncertainties related to climate change – can be implemented in theWSW sector of the Stockholm Region solely due to concerns of climate change.</p>

This thesis investigates the adaptation to climate change that is taking place in the WaterSupply and Waste Water (WSW) sector of the Stockholm Region. The adaptation processis analyzed in terms of building adaptive capacity and implementing adaptive decisions.Theories on organizational learning and the concept of an organization’s adaptation spaceare used to understand the factors that influence the adaptation process and the capacityof the studied WSW organizations to adapt to climate change. A case study approach hasbeen used and by focusing the research on four regional WSW organizations – thatcomprise a majority of the region’s WSW activities – it is argued that conclusionsrelevant to the region’s WSW sector as a whole can be made. Semi-structured interviewswith the complete management board – in three out of four organizations – and officialdocuments and reports, are the main sources of primary data for the analysis.The results show that adaptation to climate change is occuring in the WSW sector of theStockholm Region. The adaptation is mainly taking the form of building adaptivecapacity and there is only limited evidence of implementation of adaptive decisions. Theresearch suggests that there are few technical and organizational limitations foradaptation to take place and that the main factors influencing the adaptation space of thesector is how the climate change issue and risks are interpreted and perceptions of howthe WSW organizations should function and use their limited economical resources. Twoconclusions are drawn from these results: first, factors influencing the feasibility andattractiveness of different adaptation options need to be included and analysed in order tounderstand the actual adaptation space of an organization. Second, due to the factorsidentified as influencing the adaptation space, it is unlikely, at present, that robustinfrastructure solutions – which have been suggested in the literature as a viable way todeal with the intrinsic uncertainties related to climate change – can be implemented in theWSW sector of the Stockholm Region solely due to concerns of climate change.

Processing and optimizing ad-hoc and continual queries in an open environment with distributed, autonomous, and heterogeneous data servers (e.g., the Internet) pose several technical challenges. First, it is well known that optimized query execution plans constructed at compile time make some assumptions about the environment (e.g., network speed, data sources' availability). When such assumptions no longer hold at runtime, how can I guarantee the optimized execution of the query? Second, it is widely recognized that runtime adaptation is a complex and difficult task in terms of cost and benefit. How to develop an adaptation methodology that makes the runtime adaptation beneficial at an affordable cost? Last, but not the least, are there any viable performance metrics and performance evaluation techniques for measuring the cost and validating the benefits of runtime adaptation methods? To address the new challenges posed by Internet query and search systems, several areas of computer science (e.g., database and operating systems) are exploring the design of systems that are adaptive to their environment. However, despite the large number of adaptive systems proposed in the literature up to now, most of them present a solution for adapting the system to a specific change to the runtime environment. Typically, these solutions are not easily ``extendable' to allow the system to adapt to other runtime changes not predicted in their approach. In this dissertation, I study the problem of how to construct a framework where I can catalog the known solutions to query processing adaptation and how to develop an application that makes use of this framework. I call the solution to these two problems the Ginga approach. I provide in this dissertation three main contributions: The first contribution is the adoption of the Adaptation Space concept combined with feedback-based control mechanisms for coordinating and integrating different kinds of query adaptations to different runtime changes. The second contribution is the development of a systematic approach, called Ginga, to integrate the adaptation space with feedback control that allows me to combine the generation of predefined query plans (at compile-time) with reactive adaptive query processing (at runtime), including policies and mechanisms for determining when to adapt, what to adapt, and how to adapt. The third contribution is a detailed study on how to adapt to two important runtime changes, and their combination, encountered during the execution of distributed queries: memory constraints and end-to-end delays.

Self-adaptive systems are capable of autonomously adjusting their behavior at runtime to accomplish particular adaptation goals. The most common way to realize self-adaption is using a feedback loop(s) which contains four actions: collect runtime data from the system and its environment, analyze the collected data, decide if an adaptation plan is required, and act according to the adaptation plan for achieving the adaptation goals. Existing approaches achieve the adaptation goals by using formal methods, and exhaustively verify all the available adaptation options, i.e., adaptation space. However, verifying the entire adaptation space is often not feasible since it requires time and resources. In this thesis, we present an approach which uses machine learning to reduce the adaptation space in self-adaptive systems. The approach integrates with the feedback loop and selects a subset of the adaptation options that are valid in the current situation. The approach is applied on the simulator of a self-adaptive Internet of Things application which is deployed in KU Leuven, Belgium. We compare our results with a formal model based self-adaptation approach called ActivFORMS. The results show that on average the adaptation space is reduced by 81.2% and the adaptation time by 85% compared to ActivFORMS while achieving the same quality guarantees.

Self-adaptive systems have limited time to adjust their configurations whenever their adaptation goals, i.e., quality requirements, are violated due to some runtime uncertainties. Within the available time, they need to analyze their adaptation space, i.e., a set of configurations, to find the best adaptation option, i.e., configuration, that can achieve their adaptation goals. Existing formal analysis approaches find the best adaptation option by analyzing the entire adaptation space. However, exhaustive analysis requires time and resources and is therefore only efficient when the adaptation space is small. The size of the adaptation space is often in hundreds or thousands, which makes formal analysis approaches inefficient in large-scale self-adaptive systems. In this thesis, we tackle this problem by presenting an online learning approach that enables formal analysis approaches to analyze large adaptation spaces efficiently. The approach integrates with the standard feedback loop and reduces the adaptation space to a subset of adaptation options that are relevant to the current runtime uncertainties. The subset is then analyzed by the formal analysis approaches, which allows them to complete the analysis faster and efficiently within the available time. We evaluate our approach on two different instances of an Internet of Things application. The evaluation shows that our approach dramatically reduces the adaptation space and analysis time without compromising the adaptation goals.