OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data
This addresses the challenge of time-sensitive applications like disaster response by enabling real-time data processing in orbit, representing a domain-specific advancement.
The paper tackles the problem of real-time analytics for Earth observation data by introducing OrbitChain, a framework that orchestrates computational resources across satellites, enabling up to 60% more analytics workload and reducing communication overhead by up to 72% compared to existing methods.
Earth observation analytics have the potential to serve many time-sensitive applications. However, due to limited bandwidth and duration of ground-satellite connections, it takes hours or even days to download and analyze data from existing Earth observation satellites, making real-time demands like timely disaster response impossible. Toward real-time analytics, we introduce OrbitChain, a collaborative analytics framework that orchestrates computational resources across multiple satellites in an Earth observation constellation. OrbitChain decomposes analytics applications into microservices and allocates computational resources for time-constrained analysis. A traffic routing algorithm is devised to minimize the inter-satellite communication overhead. OrbitChain adopts a pipeline workflow that completes Earth observation tasks in real-time, facilitates time-sensitive applications and inter-constellation collaborations such as tip-and-cue. To evaluate OrbitChain, we implement a hardware-in-the-loop orbital computing testbed. Experiments show that our system can complete up to 60% analytics workload than existing Earth observation analytics framework while reducing the communication overhead by up to 72%.