To Adapt or Not to Adapt : A Quantification Technique for Measuring an Expected Degree of Self-Adaptation
Self-adaptation and self-organization (SASO) have been introduced to the management of technical systems as an attempt to improve robustness and administrability. In particular, both mechanisms adapt the system’s structure and behavior in response to dynamics of the environment and internal or external disturbances. By now, adaptivity has been considered to be fully desirable. This position paper argues that too much adaptation conﬂicts with goals such as stability and user acceptance. Consequently, a kind of situation-dependent degree of adaptation is desired, which deﬁnes the amount and severity of tolerated adaptations in certain situations. As a ﬁrst step into this direction, this position paper presents a quantiﬁcation approach for measuring the current adaptation behavior based on generative, probabilistic models. The behavior of this method is analyzed in terms of three application scenarios: urban trafﬁc control, the swidden farming model, and data communication protocols. Furthermore, we deﬁne a research roadmap in terms of six challenges for an overall measurement framework for SASO systems.