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By offloading computation-intensive and latency-sensitive tasks from mobile devices to the mobile edge server, mobile edge computing (MEC) has been considered as a promising technology in 5 G and beyond to reduce task delay and energy consumption. This paper considers an optimization framework of computation offloading in a MEC system with multiple devices. We aim to minimize the energy consumption of all devices with their task delay constraints by jointly optimize the communication and computation resource allocation at both the devices and the mobile edge server. Specifically, dynamic voltage scaling (DVS) technology is considered in each device to adjust the operating frequency according to its delay constraint. The formulated problem is a non-convex problem due to a couple among multiple variables. To tackle the non-convex problem, we first decompose the original problem by optimizing the offloading ratio and transmission power iteratively. Then, we proposed a joint communication and computation optimization algorithm based on the difference of convex function algorithms (DCA) to solve the optimization problem. Finally, simulation results show that the proposed joint communication and computation scheme significantly improves the energy efficiency of the devices comparing with the local computing scheme and server computing scheme.
Li et al. (Mon,) studied this question.