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Applied Sciences
Volume 10
Issue 5
10.3390/app10051663
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Article

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

by
Soohyun Park
1,
Dohyun Kwon
1,
Joongheon Kim
2,*,
Youn Kyu Lee
3,* and
Sungrae Cho
1,*
1
School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, Korea
2
School of Electrical Engineering, Korea University, Seoul 02841, Korea
3
Multimedia Processing Lab., Samsung Advanced Institute of Technology, Suwon 16677, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(5), 1663; https://doi.org/10.3390/app10051663
Received: 29 January 2020 / Revised: 25 February 2020 / Accepted: 27 February 2020 / Published: 1 March 2020
(This article belongs to the Special Issue Advances in Deep Learning Ⅱ)
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Abstract

This paper proposes a novel dynamic offloading decision method which is inspired by deep reinforcement learning (DRL). In order to realize real-time communications in mobile edge computing systems, an efficient task offloading algorithm is required. When the decision of actions (offloading enabled, i.e., computing in clouds or offloading disabled, i.e., computing in local edges) is made by the proposed DRL-based dynamic algorithm in each unit time, it is required to consider real-time/seamless data transmission and energy-efficiency in mobile edge devices. Therefore, our proposed dynamic offloading decision algorithm is designed for the joint optimization of delay and energy-efficient communications based on DRL framework. According to the performance evaluation via data-intensive simulations, this paper verifies that the proposed dynamic algorithm achieves desired performance. View Full-Text
Keywords: mobile edge computing; offloading; real-time; deep reinforcement learning; deep Q-network mobile edge computing; offloading; real-time; deep reinforcement learning; deep Q-network
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MDPI and ACS Style

Park, S.; Kwon, D.; Kim, J.; Lee, Y.K.; Cho, S. Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results. Appl. Sci. 2020, 10, 1663. https://doi.org/10.3390/app10051663

AMA Style

Park S, Kwon D, Kim J, Lee YK, Cho S. Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results. Applied Sciences. 2020; 10(5):1663. https://doi.org/10.3390/app10051663

Chicago/Turabian Style

Park, Soohyun, Dohyun Kwon, Joongheon Kim, Youn K. Lee, and Sungrae Cho. 2020. "Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results" Applied Sciences 10, no. 5: 1663. https://doi.org/10.3390/app10051663

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