A multipath routing algorithm for satellite networksbased on service demand and traffic awareness

Ziyang XING; Hui QI; Xiaoqiang DI; Jinyao LIU; Rui XU; Jing CHEN; Ligang CONG

doi:10.1631/FITEE.2200507

Your Location：

Home >

Browse articles >

A multipath routing algorithm for satellite networksbased on service demand and traffic awareness

Regular Papers | Updated：2023-07-03

- A multipath routing algorithm for satellite networksbased on service demand and traffic awareness
  Enhanced Publication
- 一种基于业务需求与流量感知的卫星网络多路径路由算法
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 6, Pages: 844-858(2023)
- Affiliations：
  
  1.Jilin Key Laboratory of Network and Information Security, Changchun 130022, China
  2.School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
  3.Information Center, Changchun University of Science and Technology, Changchun 130022, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2200507
  CLC： TP393
- Published：0 June 2023，
  
  Received：25 October 2022，
  
  Accepted：2023-03-27
- Accepted：
Scan QR Code
ZIYANG XING, HUI QI, XIAOQIANG DI, et al. A multipath routing algorithm for satellite networksbased on service demand and traffic awareness. [J]. Frontiers of information technology & electronic engineering, 2023, 24(6): 844-858.
DOI：

ZIYANG XING, HUI QI, XIAOQIANG DI, et al. A multipath routing algorithm for satellite networksbased on service demand and traffic awareness. [J]. Frontiers of information technology & electronic engineering, 2023, 24(6): 844-858. DOI： 10.1631/FITEE.2200507.

摘要

随着低轨卫星制造和发射成本的降低，以及其覆盖范围大、数据传输速率高等优点，低轨卫星已成为空地网络数据传输的重要组成部分。但受地理位置及人们生活习惯等因素影响，用户对数据需求差异会造成网络流量不均衡，可能导致网络拥塞进而影响数据传输。传统卫星网络获取网络信息收敛慢，无法细粒度收集全局网络信息，不利于计算最优路由。多业务请求无法满足服务质量要求。本文将人工智能技术应用于低轨卫星网络，利用软件定义网络获取全局网络信息，感知网络流量，通过强化学习在线制定综合决策，实时更新最优路由策略。仿真结果表明，所提强化学习算法有良好收敛性和较强泛化能力。与传统路由相比，本文算法吞吐量提高了8%，且具有负载均衡性。

Abstract

With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates

satellites have become an important part of data transmission in air-ground networks. However

due to the factors such as geographical location and people’s living habits

the differences in user’ demand for multimedia data will result in unbalanced network traffic

which may lead to network congestion and affect data transmission. In addition

in traditional satellite network transmission

the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner

which is not conducive to calculating optimal routes. The service quality requirements cannot be satisfied when multiple service requests are made. Based on the above

in this paper artificial intelligence technology is applied to the satellite network

and a software-defined network is used to obtain the global network information

perceive network traffic

develop comprehensive decisions online through reinforcement learning

and update the optimal routing strategy in real time. Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability. Compared with traditional routing

the throughput is 8% higher

and the proposed method has load balancing characteristics.

关键词

软件定义网络（SDN）快速用户数据报协议互联网连接（QUIC）强化学习Sketch多业务需求卫星网络

Keywords

Software-defined network (SDN)Quick user datagram protocol Internet connection (QUIC)Reinforcement learningSketchMulti-service demandSatellite network

references

Arfeen A, Uddin R, 2020. Quality of experience-based optimization of satellite Internet-at-sea using WAN accelerators. Int J Satell Commun Netw, 38(6):527-556. doi: 10.1002/sat.1366http://doi.org/10.1002/sat.1366

Bujari A, Luglio M, Palazzi CE, et al., 2020. A virtual PEP for web optimization over a satellite-terrestrial backhaul. IEEE Commun Mag, 58(10):42-48. doi: 10.1109/MCOM.001.2000322http://doi.org/10.1109/MCOM.001.2000322

Chen Q, Yang L, Guo DK, et al., 2022. LEO satellite networks: when do all shortest distance paths belong to minimum hop path set. IEEE Trans Aerosp Electron Syst, 58(4):3730-3734. doi: 10.1109/TAES.2022.3143090http://doi.org/10.1109/TAES.2022.3143090

Gao K, Xu CQ, Qin JR, et al., 2019. QoS-driven path selection for MPTCP: a scalable SDN-assisted approach. Proc IEEE Wireless Communications and Networking Conf, p.1-6. doi: 10.1109/WCNC.2019.8885585http://doi.org/10.1109/WCNC.2019.8885585

Han C, Huo LY, Tong XH, et al., 2020. Spatial anti-jamming scheme for Internet of satellites based on the deep reinforcement learning and Stackelberg game. IEEE Trans Veh Technol, 69(5):5331-5342. doi: 10.1109/TVT.2020.2982672http://doi.org/10.1109/TVT.2020.2982672

Huo LW, Jiang DD, Zhu XN, et al., 2022. A SDN-based fine-grained measurement and modeling approach to vehicular communication network traffic. Int J Commun Syst, 35(12):e4092. doi: 10.1002/dac.4092http://doi.org/10.1002/dac.4092

Jia ZY, Sheng M, Li JD, et al., 2020. LEO-satellite-assisted UAV: joint trajectory and data collection for Internet of Remote Things in 6G aerial access networks. IEEE Int Things J, 8(12):9814-9826. doi: 10.1109/JIOT.2020.3021255http://doi.org/10.1109/JIOT.2020.3021255

Kuhn N, Michel F, Thomas L, et al., 2020. QUIC: opportunities and threats in SATCOM. Proc 10th Advanced Satellite Multimedia Systems Conf and the 16th Signal Processing for Space Communications Workshop, p.1-7.

Langley A, Riddoch A, Wilk A, et al., 2017. The QUIC transport protocol: design and Internet-scale deployment. Proc Conf of the ACM Special Interest Group on Data Communication, p.183-196. doi: 10.1145/3098822.3098842http://doi.org/10.1145/3098822.3098842

Li X, Tang FL, Zhu YM, et al., 2022. Processing-while-transmitting: cost-minimized transmission in SDN-based STINs. IEEE/ACM Trans Netw, 30(1):243-256. doi: 10.1109/TNET.2021.3107413http://doi.org/10.1109/TNET.2021.3107413

Liu D, Zhang JK, Cui JJ, et al., 2022. Deep learning aided routing for space-air-ground integrated networks relying on real satellite, flight, and shipping data. IEEE Wirel Commun, 29(2):177-184. doi: 10.1109/MWC.003.2100393http://doi.org/10.1109/MWC.003.2100393

Liu JH, Zhao BK, Xin Q, et al., 2021. DRL-ER: an intelligent energy-aware routing protocol with guaranteed delay bounds in satellite mega-constellations. IEEE Trans Netw Sci Eng, 8(4):2872-2884. doi: 10.1109/TNSE.2020.3039499http://doi.org/10.1109/TNSE.2020.3039499

Liu LT, Shen YL, Zeng SG, et al., 2021. FO-Sketch: a fast oblivious sketch for secure network measurement service in the cloud. Electronics, 10(16):2020. doi: 10.3390/electronics10162020http://doi.org/10.3390/electronics10162020

Liu ZG, Zhu J, Zhang JM, et al., 2020. Routing algorithm design of satellite network architecture based on SDN and ICN. Int J Satell Commun Netw, 38(1):1-15. doi: 10.1002/sat.1304http://doi.org/10.1002/sat.1304

Mogensen RS, Markmoller C, Madsen TK, et al., 2019. Selective redundant MP-QUIC for 5G mission critical wireless applications. Proc IEEE 89th Vehicular Technology Conf, p.1-5. doi: 10.1109/VTCSpring.2019.8746482http://doi.org/10.1109/VTCSpring.2019.8746482

Murua J, Reviriego P, 2020. Faking elephant flows on the count min sketch. IEEE Netw Lett, 2(4):199-202. doi: 10.1109/LNET.2020.3035272http://doi.org/10.1109/LNET.2020.3035272

Oroojlooyjadid A, Nazari M, Snyder LV, et al., 2022. A deep Q-network for the beer game: deep reinforcement learning for inventory optimization. Manuf Ser Oper Manag, 24(1):285-304. doi: 10.1287/msom.2020.0939http://doi.org/10.1287/msom.2020.0939

Rabitsch A, Hurtig P, Brunstrom A, 2018. A stream-aware multipath QUIC scheduler for heterogeneous paths. Proc Workshop on the Evolution, Performance, and Interoperability of QUIC, p.29-35. doi: 10.1145/3284850.3284855http://doi.org/10.1145/3284850.3284855

Shi H, Zhang L, Zuo XT, et al., 2021. Multipath deadline-aware transport proxy for space network. IEEE Int Comput, 25(6):51-57. doi: 10.1109/MIC.2021.3112804http://doi.org/10.1109/MIC.2021.3112804

Tang L, Huang Q, Lee PPC, 2019. MV-Sketch: a fast and compact invertible sketch for heavy flow detection in network data streams. Proc IEEE INFOCOM Conf on Computer Communications, p.2026-2034. doi: 10.1109/INFOCOM.2019.8737499http://doi.org/10.1109/INFOCOM.2019.8737499

Wang F, Jiang DD, Qi S, et al., 2021. An Adaboost based link planning scheme in space-air-ground integrated networks. Mob Netw Appl, 26(2):669-680. doi: 10.1007/s11036-019-01422-4http://doi.org/10.1007/s11036-019-01422-4

Wu Q, Chen X, Zhou Z, et al., 2021. Deep reinforcement learning with spatio-temporal traffic forecasting for data-driven base station sleep control. IEEE/ACM Trans Netw, 29(2):935-948. doi: 10.1109/TNET.2021.3053771http://doi.org/10.1109/TNET.2021.3053771

Xu JP, Ai B, 2021. Deep reinforcement learning for handover-aware MPTCP congestion control in space-ground integrated network of railways. IEEE Wirel Commun, 28(6):200-207. doi: 10.1109/MWC.001.2100116http://doi.org/10.1109/MWC.001.2100116

Ya D, Bin Q, Wei N, 2021. DW-Sketch: a sketch-based scheme for realizing multi-network measurement tasks. Proc 2nd Int Conf on Computer Communication and Network Security, p.191-195. doi: 10.1109/CCNS53852.2021.00043http://doi.org/10.1109/CCNS53852.2021.00043

Yang SY, Li HW, Wu Q, 2018. Performance analysis of QUIC protocol in integrated satellites and terrestrial networks. Proc 14th Int Wireless Communications & Mobile Computing Conf, p.1425-1430. doi: 10.1109/IWCMC.2018.8450388http://doi.org/10.1109/IWCMC.2018.8450388

Yang WJ, Shu SJ, Cai L, et al., 2021. MM-QUIC: mobility-aware multipath QUIC for satellite networks. Proc 17th Int Conf on Mobility, Sensing and Networking, p.608-615. doi: 10.1109/MSN53354.2021.00093http://doi.org/10.1109/MSN53354.2021.00093

Yu ML, Jose L, Miao R, 2013. Software defined traffic measurement with OpenSketch. Proc 10th USENIX Conf on Networked Systems Design and Implementation, p.29-42.

Zhang R, Liu J, Yang D, et al., 2020. A survey on satellite networks based on software-defined networking. Front Data Comput, 2(3):3-17.

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Domain adaptation in reinforcement learning: a comprehensive and systematic study

Multi-agent reinforcement learning behavioral control for nonlinear second-order systems

An anti-collision algorithm for robotic search-and-rescue tasks in unknown dynamic environments

Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

A home energy management approach using decoupling value and policy in reinforcement learning

Related Author

Mohammad TESHNEHLAB

Arash SHARIFI

Mitra MIRZAREZAEE

Amirfarhad FARHADI

Congjie PAN

Jie HUANG

Zhenyi ZHANG

Huanhuan YANG

Related Institution

Department of Control Engineering, K.N. Toosi University of Technology

Department of Computer Engineering, Science and Research Branch, Islamic Azad University

G+ Industrial Internet Institute, Fuzhou University

College of Electrical Engineering and Automation, Fuzhou University

College of Computer, National University of Defense Technology

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰