AerialDB：一种面向无人机群的联邦点对点时空边缘数据存储
AerialDB: A Federated Peer-to-Peer Spatio-temporal Edge Datastore for Drone Fleets
AI总结：
本文提出 AerialDB，一种面向无人机群的轻量级去中心化时空边缘数据存储与查询系统，通过基于内容的副本放置和分片索引技术优化灾害管理中的时空查询，实现高效扩展、容错降级及显著优于基线的性能提升。
摘要：
近年来，利用“无人机互联网”这一最新计算范式的研究呈现前所未有的增长，该范式包含由互联的无人机（UAV）组成的机群，用于在灾害区域特有的高移动和多变环境中执行监测与分析等多种任务。鉴于部署在此类场景中的无人机群收集的典型数据（即视频和图像）可能远超机载计算机的处理能力，无人机需要将数据实时卸载到边缘和云端以进行进一步处理。为此，我们提出了 AerialDB 的设计——一种轻量级的去中心化数据存储和查询系统，能够在多无人机系统上存储和处理时间序列数据，该系统包含：A）配备机载计算机的数百架无人机群，以及 B）通过蜂窝链路连接的地面边缘服务器。利用基于内容的副本放置和分片索引的轻量级技术，AerialDB 经过优化，能够高效处理现实世界灾害管理应用执行的典型空间和时间查询的各种可能组合。通过跨越多达 400 架无人机和 80 个边缘节点的容器化部署，我们证明 AerialDB 能够高效扩展，并在不同的现实工作负载下提供近乎实时的性能。此外，AerialDB 包含一个去中心化且具备位置感知能力的分布式执行引擎，在处理大型时空数据时，即使在边缘故障的情况下也能提供相对较低延迟的性能优雅降级。与最先进的基线相比，AerialDB 展现出相当的插入性能和 100 倍的查询性能提升。此外，与云基线相比，它在插入和查询工作负载上分别实现了 10 倍和 100 倍的性能提升。
英文摘要：
Recent years have seen an unprecedented growth in research that leverages the newest computing paradigm of Internet of Drones, comprising a fleet of connected Unmanned Aerial Vehicles (UAVs) used for a wide range of tasks such as monitoring and analytics in highly mobile and changing environments characteristic of disaster regions. Given that the typical data (i.e., videos and images) collected by the fleet of UAVs deployed in such scenarios can be considerably larger than what the onboard computers can process, the UAVs need to offload their data in real-time to the edge and the cloud for further processing. To that end, we present the design of AerialDB - a lightweight decentralized data storage and query system that can store and process time series data on a multi-UAV system comprising: A) a fleet of hundreds of UAVs fitted with onboard computers, and B) ground-based edge servers connected through a cellular link. Leveraging lightweight techniques for content-based replica placement and indexing of shards, AerialDB has been optimized for efficient processing of different possible combinations of typical spatial and temporal queries performed by real-world disaster management applications. Using containerized deployment spanning up to 400 drones and 80 edges, we demonstrate that AerialDB is able to scale efficiently while providing near real-time performance with different realistic workloads. Further, AerialDB comprises a decentralized and locality-aware distributed execution engine which provides graceful degradation of performance upon edge failures with relatively low latency while processing large spatio-temporal data. AerialDB exhibits comparable insertion performance and 100 times improvement in query performance against state-of-the-art baseline. Moreover, it exhibits a 10 times and 100 times improvement with insertion and query workloads respectively over the cloud baseline.
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