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Earth observation Low Earth Orbit (LEO) satellites collect enormous amounts of data that needs to be transferred first to ground stations and then to the cloud, for storage and processing. Satellites today transmit data greedily to ground stations, with full utilization of bandwidth during each contact period. We show that due to the layout of ground stations and orbital characteristics, this approach overloads some ground stations and underloads others, leading to lost throughput and large end-to-end latency for images. We present a new end-to-end scheduler system called Umbra, which plans transfers from large satellite constellations through ground stations to the cloud, by accounting for both spatial and temporal factors, i.e., orbital dynamics, bandwidth constraints, and queue sizes. At the heart of Umbra is a new class of scheduling algorithms called withhold scheduling, wherein the sender (i.e., satellite) selectively under-utilizes some links to ground stations. We show that Umbra’s counter-intuitive approach increases throughput by 13-31% & reduces P90 latency by 3-6 X.
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Known Knowns and Unknowns: Near-realtime Earth Observation Via Query Bifurcation in Serval
Earth observation satellites, in low Earth orbits, are increasingly approaching near-continuous imaging of the Earth. Today, these satellites capture an image of every part of Earth every few hours. However, the networking capabilities haven’t caught up, and can introduce delays of few hours to days in getting these images to Earth. While this delay is acceptable for delay-tolerant applications like land cover maps, crop type identification, etc., it is unacceptable for latency-sensitive applications like forest fire detection or disaster monitoring. We design Serval to enable near-realtime insights from Earth imagery for latency-sensitive applications despite the networking bottlenecks by leveraging the emerging computational capabilities on the satellites and ground stations. The key challenge for our work stems from the limited computational capabilities and power resources available on a satellite. We solve this challenge by leveraging predictability in satellite orbits to bifurcate computation across satellites and ground stations. We evaluate Serval using trace-driven simulations and hardware emulations on a dataset comprising ten million images captured using the Planet Dove constellation comprising nearly 200 satellites. Serval reduces end-to-end latency for high priority queries from 71.71 hours (incurred by state of the art) to 2 minutes, and 90-th percentile from 149 hours to 47 minutes.
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- Award ID(s):
- 2237474
- PAR ID:
- 10499376
- Publisher / Repository:
- USENIX NSDI
- Date Published:
- Journal Name:
- USENIX Networked Systems Design and Implementation
- ISSN:
- NA
- ISBN:
- 978-1-939133-33-5
- Format(s):
- Medium: X
- Location:
- Santa Clara, California
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
