Using fuzzy policies to improve context interpretation in adaptive systems

Abstract

Adaptation is an increasingly important requirement for software systems executing in large-scale, heterogeneous, and dynamic environments. A central aspect of the adaptation methodology is management of contextual information needed to support the adaptation process. A major design challenge of managing contextual data lies in the fact that the information is partial, uncertain, and inherently suitable for diverging interpretations. While existing adaptation solutions focus on techniques, methods, and tools, the challenge of managing and interpreting ambiguous contextual information remains largely unresolved. In this paper, we present a new approach to knowledge management in adaptation feedback control loops that aims to overcome these issues by applying fuzzy set theory and approximate reasoning. Our new knowledge management scheme interprets imprecise information and effectively integrates this information into the adaptation feedback control loop. To test and evaluate our solution, we implemented it in an adaptation engine to perform rate control for media streaming applications. The evaluation results show the benefits of our approach in terms of flexibility and performance when compared to more traditional methods, such as TCP-friendly rate control.