Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment
The recent emergence of sixth-generation (6G) enabled wireless communication technology has resulted in the rapid proliferation of a wide range of real-time applications. These applications are highly data-computation intensive and generate huge data traffic. Cybertwin-driven edge computing emerges...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157822000386 |
| _version_ | 1817982236600827904 |
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| author | Vibha Jain Bijendra Kumar Aditya Gupta |
| author_facet | Vibha Jain Bijendra Kumar Aditya Gupta |
| author_sort | Vibha Jain |
| collection | DOAJ |
| description | The recent emergence of sixth-generation (6G) enabled wireless communication technology has resulted in the rapid proliferation of a wide range of real-time applications. These applications are highly data-computation intensive and generate huge data traffic. Cybertwin-driven edge computing emerges as a promising solution to satisfy massive user demand, but it also introduces new challenges. One of the most difficult challenges in edge networks is efficiently offloading tasks while managing computation, communication, and cache resources. Traditional statistical optimization methods are incapable of addressing the offloading problem in a dynamic edge computing environment. In this work, we propose a joint resource allocation and computation offloading scheme by integrating deep reinforcement learning in Cybertwin enabled 6G wireless networks. The proposed system uses the potential of the MATD3 algorithm to provide QoS to end-users by minimizing the overall latency and energy consumption with better management of cache resources. As these edge resources are deployed in inaccessible locations, therefore, we employ secure authentication mechanism for Cybertwins. The proposed system is implemented in a simulated environment, and the results are calculated for different performance metrics with previous benchmark methodologies such as RRA, GRA, and MADDPG. The comparative analysis reveals that the proposed MATD3 reduces end-to-end latency and energy consumption by 13.8% and 12.5% respectively over MADDPG with a 4% increase in successful task completion. |
| first_indexed | 2024-04-13T23:18:07Z |
| format | Article |
| id | doaj.art-0e0eb0a04e8b41b5a129108a75bf75c2 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T23:18:07Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-0e0eb0a04e8b41b5a129108a75bf75c22022-12-22T02:25:20ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-09-0134857085720Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environmentVibha Jain0Bijendra Kumar1Aditya Gupta2Netaji Subhas University of Technology, New Delhi, IndiaNetaji Subhas University of Technology, New Delhi, IndiaSRM University, Delhi-NCR, Sonepat, India; Corresponding author.The recent emergence of sixth-generation (6G) enabled wireless communication technology has resulted in the rapid proliferation of a wide range of real-time applications. These applications are highly data-computation intensive and generate huge data traffic. Cybertwin-driven edge computing emerges as a promising solution to satisfy massive user demand, but it also introduces new challenges. One of the most difficult challenges in edge networks is efficiently offloading tasks while managing computation, communication, and cache resources. Traditional statistical optimization methods are incapable of addressing the offloading problem in a dynamic edge computing environment. In this work, we propose a joint resource allocation and computation offloading scheme by integrating deep reinforcement learning in Cybertwin enabled 6G wireless networks. The proposed system uses the potential of the MATD3 algorithm to provide QoS to end-users by minimizing the overall latency and energy consumption with better management of cache resources. As these edge resources are deployed in inaccessible locations, therefore, we employ secure authentication mechanism for Cybertwins. The proposed system is implemented in a simulated environment, and the results are calculated for different performance metrics with previous benchmark methodologies such as RRA, GRA, and MADDPG. The comparative analysis reveals that the proposed MATD3 reduces end-to-end latency and energy consumption by 13.8% and 12.5% respectively over MADDPG with a 4% increase in successful task completion.http://www.sciencedirect.com/science/article/pii/S1319157822000386Cybertwin6GResource allocationComputation offloadingDeep reinforcement learning |
| spellingShingle | Vibha Jain Bijendra Kumar Aditya Gupta Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment Journal of King Saud University: Computer and Information Sciences Cybertwin 6G Resource allocation Computation offloading Deep reinforcement learning |
| title | Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment |
| title_full | Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment |
| title_fullStr | Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment |
| title_full_unstemmed | Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment |
| title_short | Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment |
| title_sort | cybertwin driven resource allocation using deep reinforcement learning in 6g enabled edge environment |
| topic | Cybertwin 6G Resource allocation Computation offloading Deep reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S1319157822000386 |
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