Abstract:

In order to reduce the total overhead of the MEC (Mobile Edge Computing) system, the weighted sum of latency and energy consumption of all devices are considered as the optimization objective, and the problem of task offloading is solved in a multi-user multi-channel mobile edge computing system. Specifically, multiple user devices are able to offload computationally-heavy tasks to the MEC server over a wireless channel. Considering the difference in residual energy among multiple smart devices, an energy factor is introduced to measure the bias of smart devices between energy consumption and latency. A reinforcement learning scheme based on the Q-learning algorithm is applied to co-optimize the offloading decision, the allocation of computational resources, and the selection of wireless channels. Simulation results show that the algorithm can effectively reduce the delay and energy consumption of task processing and accommodate more users.

Key words: mobile edge computing, computational offloading, channel selection, energy factor