Machine Learning-Based Stochastic Task Offloading Algorithm in Mobile-Edge Computing

doi:10.13190/j.jbupt.2018-078

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2019, Vol. 42 ›› Issue (2): 25-30.doi: 10.13190/j.jbupt.2018-078

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Machine Learning-Based Stochastic Task Offloading Algorithm in Mobile-Edge Computing

MENG Hao¹, HUO Ru¹, GUO Qian-ying¹, HUANG Tao^1,2, LIU Yun-jie^1,2

1. Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China;
2. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2018-04-30 Online:2019-04-28 Published:2019-04-09

Abstract

Abstract: 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.

Key words: mobile-edge computing, stochastic task offloading, machine learning, delay, mobile device's energy consumption

CLC Number:

TN929.53

MENG Hao, HUO Ru, GUO Qian-ying, HUANG Tao, LIU Yun-jie. Machine Learning-Based Stochastic Task Offloading Algorithm in Mobile-Edge Computing[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(2): 25-30.

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Machine Learning-Based Stochastic Task Offloading Algorithm in Mobile-Edge Computing

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