Full Text:   <1028>

Summary:  <268>

Suppl. Mater.: 

CLC number: TP393

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2024-02-20

Cited: 0

Clicked: 1171

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiaoqiang DI

https://orcid.org/0000-0001-9432-4564

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.6 P.791-808

http://doi.org/10.1631/FITEE.2400025


Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission


Author(s):  Ziyang XING, Xiaoqiang DI, Hui QI, Jing CHEN, Jinhui CAO, Jinyao LIU, Xusheng LI, Zichu ZHANG, Yuchen ZHU, Lei CHEN, Kai HUANG, Xinghan HUO

Affiliation(s):  Jilin Key Laboratory of Network and Information Security, Changchun 130022, China; more

Corresponding email(s):   dixiaoqiang@cust.edu.cn

Key Words:  Information-centric satellite network, Burst traffic, Content delivery, Federated reinforcement learning, Mixed-integer linear programming model, Bloom filter, Dynamic network


Ziyang XING, Xiaoqiang DI, Hui QI, Jing CHEN, Jinhui CAO, Jinyao LIU, Xusheng LI, Zichu ZHANG, Yuchen ZHU, Lei CHEN, Kai HUANG, Xinghan HUO. Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(6): 791-808.

@article{title="Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission",
author="Ziyang XING, Xiaoqiang DI, Hui QI, Jing CHEN, Jinhui CAO, Jinyao LIU, Xusheng LI, Zichu ZHANG, Yuchen ZHU, Lei CHEN, Kai HUANG, Xinghan HUO",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="25",
number="6",
pages="791-808",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400025"
}

%0 Journal Article
%T Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission
%A Ziyang XING
%A Xiaoqiang DI
%A Hui QI
%A Jing CHEN
%A Jinhui CAO
%A Jinyao LIU
%A Xusheng LI
%A Zichu ZHANG
%A Yuchen ZHU
%A Lei CHEN
%A Kai HUANG
%A Xinghan HUO
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 6
%P 791-808
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400025

TY - JOUR
T1 - Optimal replication strategy for mitigating burst traffic in information-centric satellite networks: a focus on remote sensing image transmission
A1 - Ziyang XING
A1 - Xiaoqiang DI
A1 - Hui QI
A1 - Jing CHEN
A1 - Jinhui CAO
A1 - Jinyao LIU
A1 - Xusheng LI
A1 - Zichu ZHANG
A1 - Yuchen ZHU
A1 - Lei CHEN
A1 - Kai HUANG
A1 - Xinghan HUO
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 6
SP - 791
EP - 808
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400025


Abstract: 
information-centric satellite networks play a crucial role in remote sensing applications, particularly in the transmission of remote sensing images. However, the occurrence of burst traffic poses significant challenges in meeting the increased bandwidth demands. Traditional content delivery networks are ill-equipped to handle such bursts due to their pre-deployed content. In this paper, we propose an optimal replication strategy for mitigating burst traffic in information-centric satellite networks, specifically focusing on the transmission of remote sensing images. Our strategy involves selecting the most optimal replication delivery satellite node when multiple users subscribe to the same remote sensing content within a short time, effectively reducing network transmission data and preventing throughput degradation caused by burst traffic expansion. We formulate the content delivery process as a multi-objective optimization problem and apply Markov decision processes to determine the optimal value for burst traffic reduction. To address these challenges, we leverage federated reinforcement learning techniques. Additionally, we use bloom filters with subdivision and data identification methods to enable rapid retrieval and encoding of remote sensing images. Through software-based simulations using a low Earth orbit satellite constellation, we validate the effectiveness of our proposed strategy, achieving a significant 17% reduction in the average delivery delay. This paper offers valuable insights into efficient content delivery in satellite networks, specifically targeting the transmission of remote sensing images, and presents a promising approach to mitigate burst traffic challenges in information-centric environments.

信息中心卫星网络中缓解突发流量的最优替代策略-聚集遥感图像传输

邢紫阳1,2,底晓强1,2,3,祁晖1,2,陈静1,2,曹金辉1,2,刘晋尧1,2
李旭生1,2,张子初1,2,朱玉臣1,2,陈雷1,2,黄锴1,2,霍星翰1,2
1吉林省网络与信息安全重点实验室,中国长春市,130022
2长春理工大学计算机科学技术学院,中国长春市,130022
3长春理工大学信息化中心,中国长春市,130022
摘要:信息中心卫星网络在遥感图像传输中发挥着重要作用,然而,突发业务的出现在满足日益增长的带宽需求方面带来重大挑战。传统内容传输网络(CDN)由于需要预先部署内容,不具备应对此类突发流量的能力。本文提出一种最优替代策略,用于缓解信息中心卫星网络中的突发流量,特别是针对遥感图像传输。当多个用户在短时间内订阅相同的遥感图像内容时,所提策略选择最优的替代交付卫星节点,有效减少网络传输数据,防止突发流量导致的吞吐量下降。将内容传输过程公式化为一个多目标优化问题,应用马尔可夫决策确定突发流量减少的最优值,并利用联邦强化学习求解。此外,基于布隆过滤器设计了图像划分和识别方法,快速检索编码后的遥感图像。通过软件模拟低轨道卫星星座,验证了所提策略的有效性,平均交付时延减少17%。本文为卫星网络内容高效传输,特别是遥感图像传输,提供宝贵见解,并提出一种有前景的途径缓解信息中心环境中的突发流量挑战。

关键词:信息中心卫星网络;突发流量;内容传输;联邦强化学习;混合整数线性规划模型;布隆过滤器;动态网络

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Aung N, Dhelim S, Chen LM, et al., 2023. VeSoNet: traffic-aware content caching for vehicular social networks using deep reinforcement learning. IEEE Trans Intell Transp Syst, 24(8):8638-8649.

[2]Baldoni G, Quevedo J, Guimaraes C, et al., 2023. Data-centric service-based architecture for edge-native 6G network. IEEE Commun Mag, early access.

[3]Bilal M, Kang SG, 2019. Network-coding approach for information-centric networking. IEEE Syst J, 13(2):1376-1385.

[4]Biradar AG, 2020. A comparative study on routing protocols: RIP, OSPF and EIGRP and their analysis using GNS-3. Proc 5th IEEE Int Conf on Recent Advances and Innovations in Engineering, p.1-5.

[5]Cao Y, Zhu YF, Lv JF, et al., 2020. Research on in-network caching mechanisms for space-integrated-ground content delivery service. Space-Integr-Ground Inform Netw, 1(2):48-56.

[6]Chaudhary P, Hubballi N, Kulkarni SG, 2023. eNCache: improving content delivery with cooperative caching in named data networking. Comput Netw, 237:110104.

[7]Chukhno N, Chukhno O, Pizzi S, et al., 2023. Approaching 6G use case requirements with multicasting. IEEE Commun Mag, 61(5):144-150.

[8]Deng SG, Zhang C, Li C, et al., 2021. Burst load evacuation based on dispatching and scheduling in distributed edge networks. IEEE Trans Parall Distrib Syst, 32(8):1918-1932.

[9]Guo Q, Tang FX, Kato N, 2023. Federated reinforcement learning-based resource allocation in D2D-enabled 6G. IEEE Netw, 37(5):89-95.

[10]Hazra A, Donta PK, Amgoth T, et al., 2023. Cooperative transmission scheduling and computation offloading with collaboration of fog and cloud for industrial IoT applications. IEEE Int Things J, 10(5):3944-3953.

[11]Huang YX, Yang D, Feng BH, et al., 2023. A GNN-enabled multipath routing algorithm for spatial-temporal varying LEO satellite networks. IEEE Trans Veh Technol, early access.

[12]Kwon D, Jeon J, Park S, et al., 2020. Multiagent DDPG-based deep learning for smart ocean federated learning IoT networks. IEEE Int Things J, 7(10):9895-9903.

[13]Lan SF, Ma PY, Yang GM, et al., 2023. Research and verification of new multicast BIER IPv6 technology in IP network. Proc IEEE Int Conf on Sensors, Electronics and Computer Engineering, p.1320-1325.

[14]Li F, Shen BW, Guo JL, et al., 2022. Dynamic spectrum access for Internet-of-Things based on federated deep reinforcement learning. IEEE Trans Veh Technol, 71(7):7952-7956.

[15]Li J, Xue KP, Liu JQ, et al., 2020. An ICN/SDN-based network architecture and efficient content retrieval for future satellite-terrestrial integrated networks. IEEE Netw, 34(1):188-195.

[16]Li J, Yang ZP, Wang XW, et al., 2023. Task offloading mechanism based on federated reinforcement learning in mobile edge computing. Dig Commun Netw, 9(42):492-504.

[17]Li L, Shi D, Hou RH, et al., 2020. Energy-efficient proactive caching for adaptive video streaming via data-driven optimization. IEEE Int Things J, 7(6):5549-5561.

[18]Li X, Lu LY, Ni W, et al., 2022. Federated multi-agent deep reinforcement learning for resource allocation of vehicle-to-vehicle communications. IEEE Trans Veh Technol, 71(8):8810-8824.

[19]Lin SC, Lin CH, Chu LC, et al., 2023. Enabling resilient access equality for 6G LEO satellite swarm networks. IEEE Int Things Mag, 6(3):38-43.

[20]Liu JY, Yao WB, Wang C, et al., 2023. Provisioning network slice for mobile content delivery in uncertain MEC environment. Comput Netw, 224:109629.

[21]Liu S, Huang JW, Jiang WC, et al., 2021. Reducing traffic burstiness for MPTCP in data center networks. J Netw Comput Appl, 192:103169.

[22]Liu Y, Jiang L, Qi Q, et al., 2023. Energy-efficient space–air–ground integrated edge computing for Internet of Remote Things: a federated DRL approach. IEEE Int Things J, 10(6):4845-4856.

[23]Liu ZK, Garg N, Ratnarajah T, 2024. Multi-agent federated reinforcement learning strategy for mobile virtual reality delivery networks. IEEE Trans Netw Sci Eng, 11(1):100-114.

[24]Lu L, Li Q, Zhao D, et al., 2023. Hawkeye: a dynamic and stateless multicast mechanism with deep reinforcement learning. Proc IEEE Conf on Computer Communications, p.1-10.

[25]Luglio M, Romano SP, Roseti C, et al., 2019. Service delivery models for converged satellite-terrestrial 5G network deployment: a satellite-assisted CDN use-case. IEEE Netw, 33(1):142-150.

[26]Mamatas L, Demiroglou V, Kalafatidis S, et al., 2023. Protocol-adaptive strategies for wireless mesh smart city networks. IEEE Netw, 37(2):136-143.

[27]Marler RT, Arora JS, 2010. The weighted sum method for multi-objective optimization: new insights. Struct Multid Optim, 41(6):853-862.

[28]Merling D, Stüber T, Menth M, 2023. Efficiency of BIER multicast in large networks. IEEE Trans Netw Serv Manag, 20(4):4013-4027.

[29]Miao QY, Lin H, Wang XD, et al., 2021. Federated deep reinforcement learning based secure data sharing for Internet of Things. Comput Netw, 197:108327.

[30]Narayanan A, Ramadan E, Zhang ZL, 2018. OpenCDN: an ICN-based open content distribution system using distributed actor model. Proc IEEE Conf on Computer Communications Workshops, p.268-273.

[31]Nguyen TG, Phan TV, Hoang DT, et al., 2021. Federated deep reinforcement learning for traffic monitoring in SDN-based IoT networks. IEEE Trans Cogn Commun Netw, 7(4):1048-1065.

[32]Pfandzelter T, Bermbach D, 2021. Edge (of the Earth) replication: optimizing content delivery in large LEO satellite communication networks. Proc IEEE/ACM 21st Int Symp on Cluster, Cloud and Internet Computing, p.565-575.

[33]Promwongsa N, Abu-Lebdeh M, Kianpisheh S, et al., 2020. Ensuring reliability and low cost when using a parallel VNF processing approach to embed delay-constrained slices. IEEE Trans Netw Serv Manag, 17(4):2226-2241.

[34]Promwongsa N, Ebrahimzadeh A, Glitho RH, et al., 2022. Joint VNF placement and scheduling for latency-sensitive services. IEEE Trans Netw Sci Eng, 9(4):2432-2449.

[35]Qi JJ, Zhou QH, Lei L, et al., 2021. Federated reinforcement learning: techniques, applications, and open challenges. Intell Robot, 1(1):18-57.

[36]Qiao GH, Leng SP, Maharjan S, et al., 2020. Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks. IEEE Int Things J, 7(1):247-257.

[37]Rastegar SH, Abbasfar A, Shah-Mansouri V, 2020. Rule caching in SDN-enabled base stations supporting massive IoT devices with bursty traffic. IEEE Int Things J, 7(9):8917-8931.

[38]Schwaller B, Holtzman S, George AD, 2019. Emulation-based performance studies on the HPSC space processor. Proc IEEE Aerospace Conf, p.1-11.

[39]Wang J, Rao SY, Liu Y, et al., 2023. Load balancing for heterogeneous traffic in datacenter networks. J Netw Comput Appl, 217:103692.

[40]Wang W, Zhou CH, He HL, et al., 2020. Cellular traffic load prediction with LSTM and Gaussian process regression. Proc IEEE Int Conf on Communications, p.1-6.

[41]Wang XN, Chen XL, 2023. Social attributes-based content delivery for sparse vehicular content-centric network. IEEE Trans Intell Transp Syst, 24(12):14406-14414.

[42]Wang YT, Han XF, Jin SF, 2024. Performance analysis of a VM-PM repair strategy in MEC-enabled wireless systems with bursty traffic. IEEE Trans Veh Technol, 73(1):1146-1161.

[43]Wu F, Liu XL, Wang J, et al., 2022. Research on application of space rapid response launch system based on data link. Int Conf on Neural Networks, Information, and Communication Engineering, p.353-360.

[44]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.

[45]Xie ZJ, Song SH, 2023. FedKL: tackling data heterogeneity in federated reinforcement learning by penalizing KL divergence. IEEE J Sel Areas Commun, 41(4):1227-1242.

[46]Xu MR, Peng JL, Gupta B, et al., 2022. Multiagent federated reinforcement learning for secure incentive mechanism in intelligent cyber–physical systems. IEEE Int Things J, 9(22):22095-22108.

[47]Yang H, Guo BL, Xue XW, et al., 2023. Interruption tolerance strategy for LEO constellation with optical inter-satellite link. IEEE Trans Netw Serv Manag, 20(4):4815-4830.

[48]Yu MX, Pi YB, Tang AM, et al., 2023. Coordinated parallel resource allocation for integrated access and backhaul networks. Comput Netw, 222:109533.

[49]Zha YL, Cui PS, Hu YX, et al., 2022. A scalable bitwise multicast technology in named data networking. IEICE Trans Inform Syst, E105-D(12):2104-2111.

[50]Zhang JH, Shen D, Dong F, et al., 2023. Micro-burst aware ECN in multi-queue data centers: algorithm and implementation. IEEE Trans Netw Sci Eng, early access.

[51]Zhou XK, Zheng XZ, Cui XS, et al., 2023. Digital twin enhanced federated reinforcement learning with lightweight knowledge distillation in mobile networks. IEEE J Sel Areas Commun, 41(10):3191-3211.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE