TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Multicast Tree Generation using Meta Reinforcement Learning in SDN-based Smart Network Platforms |
| 영문제목(English Title) |
Multicast Tree Generation using Meta Reinforcement Learning in SDN-based Smart Network Platforms |
| 저자(Author) |
Moonseong Kim
Woochan Lee
Jihun Chae
Namgi Kim
|
| 원문수록처(Citation) |
VOL 15 NO. 09 PP. 3138 ~ 3150 (2021. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Multimedia services on the Internet are continuously increasing. Accordingly, the demand for a technology for efficiently delivering multimedia traffic is also constantly increasing. The multicast technique, that delivers the same content to several destinations, is constantly being developed. This technique delivers a content from a source to all destinations through the multicast tree. The multicast tree with low cost increases the utilization of network resources. However, the finding of the optimal multicast tree that has the minimum link costs is very difficult and its calculation complexity is the same as the complexity of the Steiner tree calculation which is NP-complete. Therefore, we need an effective way to obtain a multicast tree with low cost and less calculation time on SDN-based smart network platforms. In this paper, we propose a new multicast tree generation algorithm which produces a multicast tree using an agent trained by model-based meta reinforcement learning. Experiments verified that the proposed algorithm generated multicast trees in less time compared with existing approximation algorithms. It produced multicast trees with low cost in a dynamic network environment compared with the previous DQN-based algorithm. |
| 키워드(Keyword) |
Hopping Sensor
Mobile IoT
Reinforcement Learning-based Protocol
Relocation Protocol
Sensory Data Networking
Simulation
Multicast tree
Meta reinforcement learning
Multimedia routing
SDN
Deep Learning
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