Energy-Efficient Multi-User Edge Computing for Streaming Tasks

In a multi-user mobile edge computing (MEC) system, mobile users can upload their own tasks to the edge server on the access network, so as to effectively reduce the processing cost of their own computing tasks, but there is a situation that task data accumulates for a long time. In a MEC system, to ensure the real-time execution of tasks with long data collecting durations, a streaming task processing scheme is proposed, where the data collection, local computing, offloading transmission, and edge computation are carried out in different time slots for a task. Under this scheme, both the task size and the actual energy consumption are related to the time length of data collection. To find the most energy-efficient way for completing the streaming tasks for the whole system, the problem of minimizing the average power consumption is formulated to jointly optimize the duration of each stage, the multi-user offloading ratio and bandwidth allocation for completing a task. Because the established optimization problem is a non-convex problem, it is difficult to solve it directly. In order to solve the intractable non-convex problem, the block coordinate descent method is utilized to separate the optimization variables into two parts. Exploiting the analytical structure of the problem, the optimal solution to the two parts of variables is obtained with bisection search and golden section search, respectively. Simulation results show that the proposed method has extremely low computational complexity and can significantly reduce the overall system power consumption. © 2024 Beijing University of Posts and Telecommunications. All rights reserved.