Using Built-In Sensors to Predict and Utilize User Satisfaction for CPU Settings on Smartphones

Understanding user experience/satisfaction with mobile systems in order to manage computational resources has become a popular approach in recent years. One of the key issues in this area is to gauge user satisfaction. In this paper, we propose and evaluate a system to save energy by altering CPU core count and frequency while keeping users satisfied. Specifically, the system uses the sensor data collected from two popular personal devices: a smartphone and a smartwatch. In the proposed architecture, we first develop prediction models by collecting sensor data along with user performance satisfaction inputs. Then, our system predicts users' current satisfaction and sets CPU core/frequency based on these predictions in real-time. We observe that sensor data gathered from these two devices are highly correlated with users' instantaneous satisfaction of the phone. We evaluate the proposed system by developing and comparing two different models. First, we develop a user-independent (user-oblivious) model by using data gathered from 10 users. Second, we develop user-dependent (personal) models for 20 different users. We demonstrate that both models can predict satisfaction with over 97% accuracy on average when a binary satisfaction model is used (i.e., users indicating satisfied versus unsatisfied). The prediction accuracy is over 91% on average if a 3-level satisfaction model is used. Our results also show that when compared to default scheme, the user-independent and user-dependent models save 8.96% and 10.12% of the total system energy on average, respectively, without impacting user satisfaction.