Cloud vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Energy Consumption

A promising technique to provide mobile applications with high computation resources is to offload the processing task to the cloud. Mobile cloud computing enables mobile devices with limited batteries to run resource hungry applications with the help of abundant processing capabilities of the clouds and to save power. However, it is not always true that cloud computing consumes less energy compared to mobile edge computing. It may take more energy for the mobile device to transmit a file to the cloud than running the task itself at the edge. This paper investigates the power minimization problem for the mobile devices by data offloading in multi-cell multi-user OFDMA mobile cloud computing networks. We consider the maximum acceptable delay and tolerable interference as QoS metrics to be satisfied in our network. We formulate the problem as a mixed integer nonlinear problem which is converted into a convex form using D.C. approximation. To solve the optimization problem, we have proposed centralized and distributed algorithms for joint power allocation and channel assignment together with decision making. Our simulation results illustrate that by utilizing the proposed algorithms, considerable power saving could be achieved e.g. about 60% for short delays and large bitstream sizes in comparison with the baselines.