Small-scale LEO Satellite Networking for Global-scale Demands
Yuanjie Li, Yimei Chen, Jiabo Yang, Jinyao Zhang, Bowen Sun, Lixin Liu, Hewu Li, Jianping Wu, Zeqi Lai, Qian Wu, Jun Liu
Abstract
Do we really need 10,000s of Low Earth Orbit (LEO) satellites to meet huge global Internet demands? While proven feasible and valuable, such LEO mega-constellation networks have raised concerns about their prohibitive capital expenditures, market monopoly, and unsustainable use of space. Instead, our analysis reveals that most of their satellites can be wasted due to their mismatch with physically uneven demands. We thus propose TinyLEO, a software-defined solution to shrink LEO network size for enormous global demands via dynamic spatiotemporal supply-demand matching. TinyLEO sparsifies satellite supplies on demand by combining diverse yet sparse orbits, hides complexities of this sparse LEO network via orbital model predictive control, and shifts the responsibility for handling these complexities to its geographic segment anycast for higher network usability, lower resource wastes, faster failovers, simpler satellites, and more flexible network orchestration. We have prototyped TinyLEO as a community toolkit for open research. Our evaluation using this toolkit shows that TinyLEO can compress the existing LEO mega-constellation network size by 2.0–7.9×, cut control plane costs by 1–3 orders of magnitude, and maintain the same demands and comparable data plane performance.
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