Battery aware stochastic QoS boosting in mobile computing devices

Hao Shen, Qiuwen Chen, Qinru Qiu

Abstract: Mobile computing has been weaved into everyday lives to a great extend. Their usage is clearly imprinted with user's personal signature. The ability to learn such signature enables immense potential in workload prediction and resource management. In this work, we investigate the user behavior modeling and apply the model for energy management. Our goal is to maximize the quality of service (QoS) provided by the mobile device (i.e., smartphone), while keep the risk of battery depletion below a given threshold. A Markov Decision Process (MDP) is constructed from history user behavior. The optimal management policy is solved using linear programing. Simulations based on real user traces validate that, compared to existing battery energy management techniques, the stochastic control performs better in boosting the mobile devices' QoS without significantly increasing the chance of battery depletion.