TY - GEN
T1 - FloodSFCP
T2 - 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023
AU - Zhang, Ruoyi
AU - Zhu, Chao
AU - Chen, Xiao
AU - Gong, Qingyuan
AU - Xie, Xinlei
AU - Bu, Xiangyuan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Prompted by the significant advancements in image processing technologies and their diverse range of applications, remote sensing satellites are poised for rapid expansion. Nonetheless, offloading the vast amount of remote sensing satellite images to the ground gateway station is inefficient due to the exorbitant costs induced by satellite links, while the limited resources of individual satellites hinder local task processing. With the advancement of the network function virtualization (NFV) technology, a new paradigm for service function chain (SFC) has emerged, which can significantly improve the flexibility and resource utilization of network services and alleviate resource conflicts by dividing large services into smaller ones organized in the form of SFCs. As mega-constellations (e.g., Starlink) developed, the number of low earth orbit (LEO) satellites is increasing. By dividing services into small sub-services and organizing them into SFCs throughout the LEO network, services that cannot be completed by a single satellite can be accomplished through multi-satellite cooperation. However, the quality of the remote sensing service is positively correlated with its latency, and the rapidly changing topology of LEO networks also adds complexity to the SFC placement. Hence, how to select appropriate satellites to place the SFC and modulate service levels, in order to obtain better remote sensing results within an acceptable latency, remains a question. To address these issues, this paper proposes the FloodSFCP, an SFC placement method that aims to increase service quality and decrease latency through offline training and online optimization via deep reinforcement learning, taking into account the variation in LEO network topology. By introducing NoisyNet, Dueling, and N-step learning, we improve the model's generalization ability and reduce the state space, thus enhancing convergence speed while reducing decision and training time. Experimental results demonstrate that FloodSFCP significantly improves service quality while reducing total decision costs.
AB - Prompted by the significant advancements in image processing technologies and their diverse range of applications, remote sensing satellites are poised for rapid expansion. Nonetheless, offloading the vast amount of remote sensing satellite images to the ground gateway station is inefficient due to the exorbitant costs induced by satellite links, while the limited resources of individual satellites hinder local task processing. With the advancement of the network function virtualization (NFV) technology, a new paradigm for service function chain (SFC) has emerged, which can significantly improve the flexibility and resource utilization of network services and alleviate resource conflicts by dividing large services into smaller ones organized in the form of SFCs. As mega-constellations (e.g., Starlink) developed, the number of low earth orbit (LEO) satellites is increasing. By dividing services into small sub-services and organizing them into SFCs throughout the LEO network, services that cannot be completed by a single satellite can be accomplished through multi-satellite cooperation. However, the quality of the remote sensing service is positively correlated with its latency, and the rapidly changing topology of LEO networks also adds complexity to the SFC placement. Hence, how to select appropriate satellites to place the SFC and modulate service levels, in order to obtain better remote sensing results within an acceptable latency, remains a question. To address these issues, this paper proposes the FloodSFCP, an SFC placement method that aims to increase service quality and decrease latency through offline training and online optimization via deep reinforcement learning, taking into account the variation in LEO network topology. By introducing NoisyNet, Dueling, and N-step learning, we improve the model's generalization ability and reduce the state space, thus enhancing convergence speed while reducing decision and training time. Experimental results demonstrate that FloodSFCP significantly improves service quality while reducing total decision costs.
KW - Deep Reinforcement Learning
KW - Service function chain
KW - quality-latency balance
KW - satellite network
UR - http://www.scopus.com/inward/record.url?scp=85177458792&partnerID=8YFLogxK
U2 - 10.1109/SECON58729.2023.10287471
DO - 10.1109/SECON58729.2023.10287471
M3 - Conference contribution
AN - SCOPUS:85177458792
T3 - Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
SP - 276
EP - 284
BT - 2023 20th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2023
PB - IEEE Computer Society
Y2 - 11 September 2023 through 14 September 2023
ER -