User Demand Prediction from Application Usage Pattern in Virtual Smartphone

The numbers of smart phone users and related applications are growing rapidly, and applications continue to become more data-intensive. In the cloud based service for smart phone, if user demand on virtual machines exceeds the hardware capacity of the server, the server incurs an overload and bottleneck, network delay, latency, and packet loss rate are increased in 3G and Wi-Fi connections. Therefore, it is important to predict user demand and to use this information for resource allocation methods such as network virtualization and load balancing. We present a novel user demand prediction method that uses analysis results of application usage patterns. By analysis of log data and using the proposed method, we can predict execution time and average volume of transmitted application data. The proposed method is mainly considered for adoption in our virtual smart phone system. We show results from an experiment performed in an implemented test-bed, including prediction results and performance of wireless media.