%0 Journal Article
%A HUANG Tao
%A HUO Ru
%A WANG Shuo
%A XUE Ning
%A ZENG Shi-qing
%T Tasks Offloading and Resource Scheduling Algorithm Based on Deep Reinforcement Learning in MEC
%D 2019
%R 10.13190/j.jbupt.2019-155
%J Journal of Beijing University of Posts and Telecommunications
%P 64-69,104
%V 42
%N 6
%X In order to improve the task offloading efficiency in multi-access edge computing (MEC), a joint optimization model for task offloading and heterogeneous resource scheduling was proposed, considering the heterogeneous communication resources and computing resources, jointly minimizing the energy consumption of user equipment, task execution delay, and the payment. A deep reinforcement learning method is adopted in the model to obtain the optimal offloading algorithm. Simulations show that the proposed algorithm improves the comprehensive indexes of equipment energy consumption, delay, and payment by 27.6%, compared to the Banker's algorithm.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2019-155