Tasks Offloading and Resource Scheduling Algorithm Based on Deep Reinforcement Learning in MEC

doi:10.13190/j.jbupt.2019-155

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2019, Vol. 42 ›› Issue (6): 64-69,104.doi: 10.13190/j.jbupt.2019-155

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Tasks Offloading and Resource Scheduling Algorithm Based on Deep Reinforcement Learning in MEC

XUE Ning¹, HUO Ru^1,2, ZENG Shi-qing³, WANG Shuo^2,3, HUANG Tao^2,3

1. Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing 100124, China;
2. Purple Mountain Laboratories, Nanjing 211111, China;
3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2019-07-11 Online:2019-12-28 Published:2019-11-15

Abstract

Abstract: 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.

Key words: multi-access edge computing, task offloading, heterogeneous resource scheduling, deep reinforcement learning

CLC Number:

TP393

XUE Ning, HUO Ru, ZENG Shi-qing, WANG Shuo, HUANG Tao. Tasks Offloading and Resource Scheduling Algorithm Based on Deep Reinforcement Learning in MEC[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(6): 64-69,104.

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Tasks Offloading and Resource Scheduling Algorithm Based on Deep Reinforcement Learning in MEC

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