Adapting large pervasive and context-aware systems. A new evolutionary-based approach

In order to enable ``anywhere, anytime'' computing, pervasive systems must autonomously adapt at runtime. The use of dynamic software product lines has emerged as a promising paradigm where well established variability management techniques are leveraged at runtime to describe evolution strategies and adaptation scenarios in terms of combinations of features. In order to identify the optimal target configuration of the system under certain circumstances, most existing approaches generate the set of valid combinations of features and return the best one. Obviously, while such approaches are well suited to small systems with a reduced number of configurations, they fail in the case of large modern pervasive systems because the generation/evaluation of all valid combinations is very costly in terms of resources and time consumption. In the present article, we introduce a new scalable, evolutionary-based approach to runtime adaptation of pervasive systems. To this end, we define the concept of transitive dependency between features and we exploit it to fasten the generation of the optimal configuration of the system. We evaluate the scalability of our proposal by reporting experimental results that show that our genetic algorithm converges in up to 90% less time than the one from the literature while preserving the exploration capabilities and solutions quality. Finally, we illustrate our proposal on the smart homes use case.