Modeling And Optimizing Runtime Adaptation Strategies At Design-Time Using Evolutionary Algorithms (Idea Paper)

Self-adaptive (software) systems (SASs) dynamically adjust themselves to environmental changes during runtime (RT) to uphold Quality of Service (QoS) objectives. Designing and optimizing the adaptation strategies for SASs, particularly in relation to their impact on quality attributes, presents a significant challenge. The extensive design space of adaptation strategies typically requires automated exploration, as manual exploration is usually infeasible. While most existing approaches focus on RT optimization, which requires the implementation of the system, we examine the optimization of runtime adaptation strategies during design-time (DT) which we consider more effective in achieving QoS goals compared to purely RT-optimized strategies. Furthermore, DT analysis offers heuristically optimized strategies prior to implementation, enhancing quality properties such as performability. We aim to complement RT optimization by proposing a Model-based quality analysis (MBQA) approach at design-time that optimizes MAPE-K based adaptation strategies across all phases. In contrast, current approaches typically focus on optimizing specific phases, such as the analysis or planning phase, rather than the strategy as a whole. In this paper, we present a comprehensive DT approach for the optimization of adaptation strategies using evolutionary algorithms.