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This paper proposes Space Data Centers (SDCs) as a viable solution to mitigate latency and bandwidth congestion by moving AI-driven processing from Earth into low Earth orbit. The study details the architectural requirements for such constellations, including orbital design, inter-satellite networking, and software orchestration, while assessing their economic feasibility for applications like Earth observation. By enabling on-orbit data analysis, this approach fundamentally shifts where critical AI inference occurs, addressing scalability limits of traditional ground station networks.
Deep Tech to Space: Space Data Centers and AI Revolution at the Edge
arXiv:2605.19892v2 Announce Type: replace-cross Abstract: Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresponding surge in space-generated data. As this trend continues, transmitting large volumes of data to Earth for processing may become increasingly costly and challenging due to potential space-to-Earth link congestion and increased latency. Moreover, traditional ground station networks may face difficulties accommodating growing data flows and workloads because of capacity constraints, complex scheduling logistics, and restricted visibility windows, which can limit scalability. Space data centers (SDCs) -- software-driven, multi-tenant artificial intelligence-based service platforms capable of processing data in orbit to generate actionable insights for client satellites and ground users -- represent a promising approach to address these challenges. This article presents the architecture of a low Earth orbit SDC satellite constellation, considering orbital design, inter-satellite links and network topology, computational resource organization, and software service orchestration. We analyze the potential technical feasibility and economic viability of SDCs using forecasting models informed by technology roadmaps and illustrate the concept through Earth observation and lunar exploration use cases.
Abstract:Dramatic cost reductions driven by private sector innovations have led to a rapid increase in the number of satellites in orbit and a corresponding surge in space-generated data. As this trend continues, transmitting large volumes of data to Earth for processing may become increasingly costly and challenging due to potential space-to-Earth link congestion and increased latency. Moreover, traditional ground station networks may face difficulties accommodating growing data flows and workloads because of capacity constraints, complex scheduling logistics, and restricted visibility windows, which can limit scalability. Space data centers (SDCs) -- software-driven, multi-tenant artificial intelligence-based service platforms capable of processing data in orbit to generate actionable insights for client satellites and ground users -- represent a promising approach to address these challenges. This article presents the architecture of a low Earth orbit SDC satellite constellation, considering orbital design, inter-satellite links and network topology, computational resource organization, and software service orchestration. We analyze the potential technical feasibility and economic viability of SDCs using forecasting models informed by technology roadmaps and illustrate the concept through Earth observation and lunar exploration use cases.
Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Networking and Internet Architecture (cs.NI)
arXiv:2605.19892 [cs.DC]
(or arXiv:2605.19892v2 [cs.DC] for this version)
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