Falcon: Towards Fast and Scalable Data Delivery for Emerging Earth Observation Constellations

Mingyang Lyu, Qian Wu, Zeqi Lai, Hewu Li, Yuanjie Li, Jun Liu

Abstract: Exploiting a constellation of small satellites to realize continuous earth observations (EO) is gaining popularity. Large-volume EO data acquired from space needs to be transferred to the ground. However, existing EO delivery approaches are either: (a) efficiency-limited, suffering from long delivery completion time due to the intermittent ground-space communication, or (b) scalability-limited since they fail to support concurrent delivery for multiple satellites in an EO constellation.To make big data delivery for emerging EO constellations fast and scalable, we propose Falcon, a multi-path EO delivery framework that wisely exploits diverse paths in broadband constellations to collaboratively deliver EO data effectively. In particular, we formulate the constellation-wide EO data multi-path download (CEOMD) problem, which aims at minimizing the delivery completion time of requested data for all EO sources. We prove the hardness of solving CEOMD, and further present a heuristic multipath routing and bandwidth allocation mechanism to tackle the technical challenges caused by time-varying satellite dynamics and flow contention, and solve the CEOMD problem efficiently. Evaluation results based on public orbital data of real EO constellations show that as compared to other state-of-the-art approaches, Falcon can reduce at least 51% delivery completion time for various data requests in large EO constellations.