Design-time Performability Optimization of Runtime Adaptation Strategies

Abstract

Self-Adaptive Systems (SASs) adapt themselves to environmental changes during runtime to maintain Quality of Service (QoS) goals. Designing and optimizing the adaptation strategy of an SAS regarding its impact on quality properties is a challenging problem. Usually the design space of adaptation strategies is too large to be explored manually and, hence, requires automated support to find optimal strategies. Most approaches address this problem with optimization at runtime requiring the system is already implemented. However, one expects design-time optimized adaptation strategies to more effectively maintain QoS goals than purely runtime optimized strategies. Also formal guarantees benefit from designed and analysed strategies. We claim that design-time analysis and optimization of adaptation strategies improve in particular quality properties such as performability. To address the research gap between runtime optimization and the ability to make statements on the achieved quality, we envision an approach that builds upon the concept of Model-Based Quality Analysis (MBQA). Many approaches in MBQA address single aspects such as formal languages for adaptation strategies, architectural description languages or QoS prediction. However, they lack integration, which leads, for example to prediction approaches assuming rather static systems. In this paper, we envision an unified approach by considering several sub-approaches as building blocks for performability-based optimization of adaptation strategies at design-time.