Application Sequence Prediction for Energy Consumption Reduction in Mobile Systems

The success of mobile devices as measured by market penetration is undeniable. Tablets and Ultrabooks are enjoying similar growing enthusiasm among the worldwide population. By offering an ever increasing number of functionalities, these devices turned to be even more subject to energy constraints than the previous generation. Limited battery autonomy becomes a major user concern and cause of dissatisfactions. Therefore, managing efficiently the energy consumption can improve mobile systems reliability, battery life as well as user experience. In this paper we propose a new approach to optimize mobile devices energy efficiency based on use patterns detection. By identifying and classifying users behaviors, we can significantly improve over the platforms stock power managers. To do so, a run-time service is proposed to collect usage data, which in turn can be mined using machine learning techniques. Our approach allows us to predict future applications usages, so the CPU frequency, Wi-Fi connectivity and the playback sound-levels can be optimized while meeting the applications and the users requirements. Our experimental results show that the proposed solution can lower the energy consumption by up to 20% vs. the out-of-the-box power governor, while maintaining a negligible system overhead.