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%.
翻译:地球观测分析具有服务众多时效性应用的潜力。然而,由于地面-卫星连接带宽有限且持续时间短,现有地球观测卫星的数据下载与分析通常需要数小时甚至数天,导致诸如灾害及时响应等实时需求无法实现。为实现实时分析,本文提出OrbitChain——一种协同分析框架,用于协调地球观测星座中多颗卫星的计算资源。OrbitChain将分析应用分解为微服务,并为时间受限的分析任务分配计算资源。设计了一种流量路由算法以最小化星间通信开销。OrbitChain采用流水线工作流,可实时完成地球观测任务,支持时效性应用及跨星座协同(如提示-响应协作)。为评估OrbitChain,我们构建了硬件在环的轨道计算测试平台。实验表明,相较于现有地球观测分析框架,本系统可多完成高达60%的分析负载,同时将通信开销降低最多72%。