To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation

Abstract

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.

Expressing the Adaptation Intent as a Sustainability Goal

“…For example, Tao Chen et al [2] introduce the term temporal adaptation debt, i.e., a technical debt which captures the economic health (e.g., net debt) of an adaptive system and indicates whether an adaptation should be performed or not (e.g., to obtain net profit) at runtime. A situation-dependent approach to measure the degree of adaptation to avoid possible conflicts between the benefits of adaptation and stability issues in the system or user acceptance is shown in [21]. The leading idea is to adapt if the adaptation has an overall positive effect in the system, or avoid adaptation otherwise.…”

The Vision of Self-Management in Cognitive Organic Power Distribution Systems

“…Self-configuration (also called self-adaptation or re-parameterisation [21]) in intelligent systems is either realised statically or adaptive. Static solutions are assumed to operate in fully predictable environments.…”

Self-improving system integration: Mastering continuous change

“…However, metrics have been proposed for assessing the integration of data quality in data warehouse development. This can be combined with metrics quantifying a degree of self-adaptation [61] or self-organisation [21] for the basic SASO capabilities. The recent effort focuses on determining measurable qualities for system integration status [19].…”