Chih-Han Yu, Shie Mannor, Georgios Theocharous, Avi Pfeffer
Advances in hardware and wireless technology have made mobile devices ubiquitous in our daily life. Consequently, extending the battery life has become a major challenge needed to improve the usability of laptops. The purpose of a power management (PM) policy is to prolong a laptop's battery life while not affecting the system performance as perceived by the user. The optimal strategy is to turn off certain components when their services are not going to be needed and turning them back on just before they are needed. This uncertainty about the future is the core challenge in PM. The main contribution of our work is to demonstrate the importance of incorporating a user model into adaptive PM. We use a Dynamic Bayesian Network (DBN) to capture the relationship between the latent state of the user and his/her observable activities. The DBN model is learned from the user's data and is therefore adapted to individual user. Besides, the future idle duration probability density functions (PDF) differ significantly when conditioned on different latent states. Based on the PDF associated with each individual latent state, our estimated future idle duration is more accurate. This information allows us to estimate the expected power savings and the expected next service requested time. By trading-off these two factors, the devised PM strategy is able to adjust to different level of aggressiveness. Furthermore, the PM decisions are also calibrated according to the latent states of different users.
Subjects: 1.6.1 Automated Device Modeling; 3.4 Probabilistic Reasoning
Submitted: Apr 10, 2007