Abstract: In a multi-user mobile edge computing (MEC) system, mobile users can upload their own tasks to the edge server on the access network, thereby effectively reducing the processing overhead of their tasks. In a MEC system, to ensure the real-time execution of tasks with long data collecting duration, a streaming task processing scheme is proposed, where the data collection and local computing, the offloading transmission and edge computation, are carried out in different time slots. Under this scheme, specific size of the task, more importantly energy consumption for executing the task, is related to the time length of data collection. To find the most energy-efficient way for completing the streaming tasks, the problem of minimizing the overall power consumption is formulated to jointly optimize the duration of each stage for completing the task, together with the multi-user offloading ratio and bandwidth allocation. In order to solve the intractable non-convex problem, block coordinate descent method is utilized to separate the optimization variables into two parts. Exploiting the analytical structure of the problem, optimal solution of these two parts of variables is obtained with bisection search and golden section search. Simulation results show that the proposed method has extremely low computational complexity and can significantly reduce the overall system power consumption.

Key words: mobile edge computing, energy efficiency, multiple users, task offloading, resource allocation