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,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/5/1663 |
| _version_ | 1828311543089987584 |
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| author | Soohyun Park Dohyun Kwon Joongheon Kim Youn Kyu Lee Sungrae Cho |
| author_facet | Soohyun Park Dohyun Kwon Joongheon Kim Youn Kyu Lee Sungrae Cho |
| author_sort | Soohyun Park |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T16:02:16Z |
| format | Article |
| id | doaj.art-92e4673ea0b743ea9d4595bbab70c4d5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-13T16:02:16Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-92e4673ea0b743ea9d4595bbab70c4d52022-12-22T02:40:31ZengMDPI AGApplied Sciences2076-34172020-03-01105166310.3390/app10051663app10051663Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation ResultsSoohyun Park0Dohyun Kwon1Joongheon Kim2Youn Kyu Lee3Sungrae Cho4School of Computer Science and Engineering, Chung-Ang University, Seoul 06974, KoreaSchool of Computer Science and Engineering, Chung-Ang University, Seoul 06974, KoreaSchool of Electrical Engineering, Korea University, Seoul 02841, KoreaMultimedia Processing Lab., Samsung Advanced Institute of Technology, Suwon 16677, KoreaSchool of Computer Science and Engineering, Chung-Ang University, Seoul 06974, KoreaThis 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.https://www.mdpi.com/2076-3417/10/5/1663mobile edge computingoffloadingreal-timedeep reinforcement learningdeep q-network |
| spellingShingle | Soohyun Park Dohyun Kwon Joongheon Kim Youn Kyu Lee Sungrae Cho Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results Applied Sciences mobile edge computing offloading real-time deep reinforcement learning deep q-network |
| title | Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results |
| title_full | Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results |
| title_fullStr | Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results |
| title_full_unstemmed | Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results |
| title_short | Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results |
| title_sort | adaptive real time offloading decision making for mobile edges deep reinforcement learning framework and simulation results |
| topic | mobile edge computing offloading real-time deep reinforcement learning deep q-network |
| url | https://www.mdpi.com/2076-3417/10/5/1663 |
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