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A SDN improvement scheme for multi‐path QUIC transmission in satellite networks.

Authors :
Ma, Hongxin
Wang, Meng
Lv, Hao
Liu, Jinyao
Di, Xiaoqiang
Qi, Hui
Source :
Computational Intelligence. Jun2024, Vol. 40 Issue 3, p1-21. 21p.
Publication Year :
2024

Abstract

In recent years, with the development of low‐earth orbit broadband satellites, the combination of multi‐path transmission and software‐defined networking (SDN) for satellite networks has seen rapid advancement. The integration of SDN and multi‐path transmission contributes to improving the efficiency of transmission and reducing network congestion. However, the current SDN controllers do not support the multi‐path QUIC protocol (MPQUIC), and the routing algorithm used in current satellite networks based on minimum hop count struggles to meet the real‐time requirements for some applications. Therefore, this paper designs and implements an SDN controller that supports the MPQUIC protocol and proposes a multi‐objective optimization‐based routing algorithm. This algorithm selects paths with lower propagation delays and higher available bandwidth for subflow transmission to improve transmission throughput. Considering the high‐speed mobility of satellite nodes and frequent link switching, this paper also designs a flow table update algorithm based on the predictability of satellite network topology. It enables proactive rerouting upon link switching, ensuring stable transmission. The performance of the proposed solution is evaluated through satellite network simulation environments. The experimental results highlight that SDN‐MPQUIC significantly improves performance metrics: it reduces average completion time by 37.3% to 59.3% compared to QSMPS and by 52.8% to 72.4% compared to Disjoint for files with different sizes. Additionally, SDN‐MPQUIC achieves an average throughput improvement of 81.4% compared to QSMPS and 147.8% compared to Disjoint, while demonstrating a 26.3% lower retransmission rate than QSMPS. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08247935
Volume :
40
Issue :
3
Database :
Academic Search Index
Journal :
Computational Intelligence
Publication Type :
Academic Journal
Accession number :
178049022
Full Text :
https://doi.org/10.1111/coin.12650