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A promising technique to provide mobile applications with high computationresources is to offload the processing task to the cloud. Utilizing theabundant processing capabilities of the clouds, mobile edge computing enablesmobile devices with limited batteries to run resource hungry applications andto save power. However, it is not always true that edge computing consumes lesspower compared to device computing. It may take more power for the mobiledevice to transmit a file to the cloud than running the task itself. This paperinvestigates the power minimization problem for the mobile devices by dataoffloading in multi-cell multi-user OFDMA mobile edge computing networks. Weconsider the maximum acceptable delay as QoS metric to be satisfied in ournetwork. We formulate the problem as a mixed integer nonlinear problem which isconverted into a convex form using D.C. approximation. To solve the convertedoptimization problem, we have proposed centralized and distributed algorithmsfor joint power allocation and channel assignment together withdecision-making. Simulation results illustrate that by utilizing the proposedalgorithms, considerable power savings can be achieved, e.g., about 60 % forlarge bit stream size compared to local computing baseline.

Device vs Edge Computing for Mobile Services: Delay-aware Decision Making to Minimize Power Consumption