Performance-aware Energy Optimization on Mobile Devices in Cellular Network

In cellular networks, it is important to conserve energy while at the same time ensuring users to have good transmission experiences. The energy cost can result from tail energy due to the radio resource control strategies designed in cellular networks and data transmission. Existing efforts generally consider one of the energy issues, and also ignore the adverse impact on user transmission performance due to energy conservation. In addition, many existing algorithms are based on prediction and knowledge on future traffic, which are hard to apply in a practical wireless system with dynamic user traffic and channel condition. The goal of this work is to design an efficient online scheduling algorithm to minimize energy consumption both due to tail energy and transmissions while meeting user performance expectation. We prove the problem to be NP-hard, and design a practical online scheduling algorithm PerES to minimize the total energy cost of multiple mobile applications subject to user performance constraints. We propose a comprehensive performance cost metric to capture the impacts due to task delay, deadline violation, different application profiles and user preferences. We prove that our proposed scheduling algorithm can make the energy consumption arbitrarily close to that of the optimal scheduling solution. The evaluation results demonstrate the effectiveness of our scheme and its higher performance than peers. Moreover, by supporting dynamic performance requirement by mobile users, PerES can achieve 2 times faster convergence to both the performance degradation bound and optimal energy conversation bound than those of traditional static methods. Using 821 million traffic flows collected from a commercial cellular carrier, we verify our scheme could achieve on average 32%-56% energy savings with different levels of user experience.