Metrics on feature models to optimize configuration adaptation at run time

Abstract

Feature models are widely used to capture variability, commonalities and configuration rules of software systems. We apply this technique to model component-based systems with many variants during specification, implementation, or run time. This representation allows us to determine the set of valid configurations befitting a given context, especially at run time. A key challenge is to determine the configuration most suitable, especially with respect to non-functional aspects: quality of service, performance, reconfiguration time... We propose an algorithm for selecting the configuration that optimizes a given quality metrics. This algorithm is a variant of the Best-First Search algorithm, a heuristic technique suitable for feature model optimization. The algorithm is parameterized with several strategies and heuristics on feature models leading to different optimality and efficiency properties. We discuss the algorithm, its strategies and heuristics, and we present experimental results showing that the algorithm meets the requirements for our real time systems.