Research Article
Routing Planning for Video Transmission in Cloud Content Delivery Networks Based on Q-Learning
@INPROCEEDINGS{10.1007/978-3-031-67162-3_29, author={Pingshan Liu and Yemin Sun and Chunyan Qin and Yiqian Liu}, title={Routing Planning for Video Transmission in Cloud Content Delivery Networks Based on Q-Learning}, proceedings={Communications and Networking. 18th EAI International Conference, ChinaCom 2023, Sanya, China, November 18--19, 2023, Proceedings}, proceedings_a={CHINACOM}, year={2024}, month={8}, keywords={Cloud Content Delivery Networks Video Transmission Q-learning End-to-End delay}, doi={10.1007/978-3-031-67162-3_29} }- Pingshan Liu
Yemin Sun
Chunyan Qin
Yiqian Liu
Year: 2024
Routing Planning for Video Transmission in Cloud Content Delivery Networks Based on Q-Learning
CHINACOM
Springer
DOI: 10.1007/978-3-031-67162-3_29
Abstract
In the cloud content delivery networks, when an edge CDN node lacks the video requested by a user, it needs to send video requests to the origin server or other edge CDN nodes. To enhance user experience quality, the target node that receives the video request needs to establish a low-latency video transmission path to send videos to the requesting node. However, existing video transmission strategies have not fully considered the dynamic network congestion status within the cloud content delivery network. Therefore, this paper proposes a Q-learning based Adaptive Video Routing algorithm (Q-AVR) specifically for video transmission issues within cloud content delivery networks. It aims to reduce end-to-end video transmission latency and improve network bandwidth utilization. In the video transmission path construction process, edge CDN nodes exchange information through sending data packets. Each node stores the information in a Q-table and makes routing decisions based on the Q values. This algorithm optimizes end-to-end video transmission by learning network status in real-time. After simulation validation, the results show that the Q-AVR algorithm can effectively reduce end-to-end video transmission latency and improve network bandwidth utilization.
