PolyDroid: Learning-Driven Specialization of Mobile Applications
This addresses the need for efficient app specialization for mobile users, but it is incremental as it builds on existing configuration and learning techniques.
The authors tackled the problem of specializing mobile apps based on user preferences to optimize resource usage, and they demonstrated that PolyDroid achieves over 85% of optimal performance with only two user experiments on a benchmark of 20 Android apps.
The increasing prevalence of mobile apps has led to a proliferation of resource usage scenarios in which they are deployed. This motivates the need to specialize mobile apps based on diverse and varying preferences of users. We propose a system, called PolyDroid, for automatically specializing mobile apps based on user preferences. The app developer provides a number of candidate configurations, called reductions, that limit the resource usage of the original app. The key challenge underlying PolyDroid concerns learning the quality of user experience under different reductions. We propose an active learning technique that requires few user experiments to determine the optimal reduction for a given resource usage specification. On a benchmark suite comprising 20 diverse, open-source Android apps, we demonstrate that on average, PolyDroid obtains more than 85% of the optimal performance using just two user experiments.