Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning
Even though wireless networks are inevitable in mobile or infrastructure-less communication systems, such as vehicle-to-everything (V2X) infrastructure in automobile, precise formation control of unmanned vehicles (UVs), or other industries that employ ad hoc deployment of systems, operation and mai...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9950062/ |
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| author | Hyeontae Joo Sangmin Lee Seunghwan Lee Hwangnam Kim |
| author_facet | Hyeontae Joo Sangmin Lee Seunghwan Lee Hwangnam Kim |
| author_sort | Hyeontae Joo |
| collection | DOAJ |
| description | Even though wireless networks are inevitable in mobile or infrastructure-less communication systems, such as vehicle-to-everything (V2X) infrastructure in automobile, precise formation control of unmanned vehicles (UVs), or other industries that employ ad hoc deployment of systems, operation and maintenance of network applications additionally impose time constraints on the wireless network. Such the requirement poses an immediate challenge to the time-sensitive aspects of devices, applications and network control, which has been addressed in the realm of time-sensitive networking (TSN). Meanwhile, software-defined networking (SDN) has successfully presented its efficiencies in ensuring quality of service for network traffic to accommodate many functions of network control and management. In this regard, we propose a traffic engineering solution based on reinforcement learning (RL) to implement TSN links with SDN over a wireless network, then optimize the quality of TSN links, and protect background traffic from excessive resource allocation for TSN-enabled but SDN-supported traffic. We implemented SDN-based TSN on a real testbed, consisting of real nodes as single board computers (SBCs) and an SDN controller, and applied RL-based network control solution to the network. The empirical results are promising in that the jitter of time-constrained traffic is improved by 24.6% and throughput of background traffic is increased by 6.5%, compared to the manual configuration mode. |
| format | Article |
| id | doaj-art-7003aba48fd549fa8f2922b07d6fc200 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7003aba48fd549fa8f2922b07d6fc2002025-08-20T02:39:11ZengIEEEIEEE Access2169-35362022-01-011011949611950510.1109/ACCESS.2022.32220609950062Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement LearningHyeontae Joo0https://orcid.org/0000-0002-3753-364XSangmin Lee1https://orcid.org/0000-0003-2554-2749Seunghwan Lee2Hwangnam Kim3https://orcid.org/0000-0003-4322-8518Department of Electrical Engineering, Korea University, Seoul, Republic of KoreaDepartment of Electrical Engineering, Korea University, Seoul, Republic of KoreaDepartment of Smart Convergence, Korea University, Seoul, Republic of KoreaDepartment of Electrical Engineering, Korea University, Seoul, Republic of KoreaEven though wireless networks are inevitable in mobile or infrastructure-less communication systems, such as vehicle-to-everything (V2X) infrastructure in automobile, precise formation control of unmanned vehicles (UVs), or other industries that employ ad hoc deployment of systems, operation and maintenance of network applications additionally impose time constraints on the wireless network. Such the requirement poses an immediate challenge to the time-sensitive aspects of devices, applications and network control, which has been addressed in the realm of time-sensitive networking (TSN). Meanwhile, software-defined networking (SDN) has successfully presented its efficiencies in ensuring quality of service for network traffic to accommodate many functions of network control and management. In this regard, we propose a traffic engineering solution based on reinforcement learning (RL) to implement TSN links with SDN over a wireless network, then optimize the quality of TSN links, and protect background traffic from excessive resource allocation for TSN-enabled but SDN-supported traffic. We implemented SDN-based TSN on a real testbed, consisting of real nodes as single board computers (SBCs) and an SDN controller, and applied RL-based network control solution to the network. The empirical results are promising in that the jitter of time-constrained traffic is improved by 24.6% and throughput of background traffic is increased by 6.5%, compared to the manual configuration mode.https://ieeexplore.ieee.org/document/9950062/Reinforcement learningtime-sensitive networkresource allocationtraffic control |
| spellingShingle | Hyeontae Joo Sangmin Lee Seunghwan Lee Hwangnam Kim Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning IEEE Access Reinforcement learning time-sensitive network resource allocation traffic control |
| title | Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning |
| title_full | Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning |
| title_fullStr | Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning |
| title_full_unstemmed | Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning |
| title_short | Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning |
| title_sort | optimizing time sensitive software defined wireless networks with reinforcement learning |
| topic | Reinforcement learning time-sensitive network resource allocation traffic control |
| url | https://ieeexplore.ieee.org/document/9950062/ |
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