SkyOctopus：通过多锚点实现低时延移动卫星网络
SkyOctopus: Enabling Low-Latency Mobile Satellite Network through Multiple Anchors
AI总结：
针对移动卫星网络单锚点架构导致的路由迂回问题，本文提出 SkyOctopus 多锚点架构，通过星载流量分类器和多路径监测选择最优锚点，实验显示端到端时延最高降低53%。
摘要：
低地球轨道（LEO）卫星星座的快速部署，使非地面网络（NTN）在提供全球通信服务方面的潜力受到关注。电信运营商正尝试与卫星网络提供商合作，开发移动卫星网络，作为地面网络的有效补充。然而，当前移动卫星网络架构仍沿用地面移动网络的单锚点设计，导致用户路由严重迂回，并显著影响其服务体验。为减少路由迂回造成的不必要时延，并为用户提供低时延的全球互联网服务，本文提出 SkyOctopus，这是一种先进的多锚点移动卫星网络架构。SkyOctopus 创新性地在卫星上部署流量分类器，以实现用户与分布在全球的多个锚点之间的连接。它通过监测多条端到端路径，保证为每个用户的目标服务器选择最优锚点。我们使用增强版 Open5GS 和 UERANSIM 构建了 SkyOctopus 原型，并由 Starlink、Kuiper 和 OneWeb 等实际 LEO 卫星星座驱动。我们开展了大量实验，结果表明，与标准 5G NTN 以及另外两种现有方案相比，SkyOctopus 可将端到端时延最高降低 53%。
英文摘要：
The rapid deployment of low earth orbit (LEO) satellite constellations has drawn attention to the potential of nonterrestrial networks (NTN) in providing global communication services. Telecom operators are attempting to collaborate with satellite network providers to develop mobile satellite networks, which serve as an effective supplement to terrestrial networks. However, current mobile satellite network architectures still employ the single-anchor design of terrestrial mobile networks, leading to severely circuitous routing for users and significantly impacting their service experience. To reduce unnecessary latency caused by circuitous routing and provide users with low-latency global internet services, this paper presents SkyOctopus, an advanced multi-anchor mobile satellite network architecture. SkyOctopus innovatively deploys traffic classifiers on satellites to enable connections between users and multiple anchor points distributed globally. It guarantees optimal anchor point selection for each user's target server by monitoring multiple end-to-end paths. We build a prototype of SkyOctopus using enhanced Open5GS and UERANSIM, which is driven by actual LEO satellite constellations such as Starlink, Kuiper, and OneWeb. We conducted extensive experiments, and the results demonstrate that, compared to standard 5G NTN and two other existing schemes, SkyOctopus can reduce end-to-end latency by up to 53\%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
