iMUTE: Energy-optimal update policy for perishable mobile contents

Mobile applications that provide ever-changing information such as social media and news feeds applications are designed to consistently update their contents in the background. This operation, often called “prefetching”, provides the users with immediate access to up-to-date contents. However, such updates often result in the unwanted side-effect of draining the battery of mobile devices. It is considered as pure waste when updated contents are not accessed before being renewed. In this paper, we develop an optimal strategy to update the contents in the background under a given energy constraint. The key challenge is to predict when the user will access the contents in a probabilistic manner from the statistics of the accessed patterns in the past. We model our problem as a constrained Markov decision process (C-MDP) and propose to tackle its high complexity with a two-step solution that combines: (1) a threshold-based backward induction algorithm for the Lagrangian relaxation of our C-MDP, and (2) an iterative root finding algorithm, iMUTE (iterative Method for optimal UpdaTe policy with Energy constraint). We prove that iMUTE converges superlinearly to the optimal solution of the original C-MDP under a mild condition. We also experimentally verify that iMUTE outperforms the periodic policy as well as the additive and multiplicative increase policies that are adopted in the Doze mode of Android systems and HUSH, in terms of user experience and energy saving.