%0 Journal Article
%A GUO Qian-ying
%A HUANG Tao
%A HUO Ru
%A LIU Yun-jie
%A MENG Hao
%T Machine Learning-Based Stochastic Task Offloading Algorithm in Mobile-Edge Computing
%D 2019
%R 10.13190/j.jbupt.2018-078
%J Journal of Beijing University of Posts and Telecommunications
%P 25-30
%V 42
%N 2
%X For mobile-edge computing (MEC), a machine learning-based stochastic task offloading algorithm was proposed. By dividing the task into offloadable components and unoffloadable components, the improved Q learning and deep learning algorithm were used to generate the optimal offloading strategy of stochastic task, which minimized the weighted sum of energy consumption and time delay of the mobile devices. The simulation results show that the proposed algorithm saves the weighted sum of energy consumption and time delay by 38.1%, compared to the local execution algorithm.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2018-078