Abstract
Smartphones play an important role in human’s life. People interact with smartphones to perform various activities. More and more mobile applications involve real-time interactions with users. That is, the operation of mobile applications relies on user behavior. However, User behaviors are affected by user contexts. In a certain context, user will perform a fixed behavior preference. Thus, we can infer the user behaviors from user contexts. Moreover, the workloads of the interactive applications can also be estimated. In this paper, we propose a context-based predictive model of smartphone workloads, which aim to build the correlation between user contexts and user behaviors. We apply machine learning methodology to user context classification. By this model, we further predict the workloads of the interactive applications. Our result shows that the average estimated CPU utilization error between predictive result and actual usage is 4.21% to 6.34%.