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

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

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,...

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Bibliographic Details
Main Authors: Soohyun Park, Dohyun Kwon, Joongheon Kim, Youn Kyu Lee, Sungrae Cho
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Applied Sciences
Subjects:
mobile edge computing
offloading
real-time
deep reinforcement learning
deep q-network
Online Access:https://www.mdpi.com/2076-3417/10/5/1663

Internet

https://www.mdpi.com/2076-3417/10/5/1663

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