Songtao He, F. Bastani, Arjun Balasingam, Karthik Gopalakrishna, Ziwen Jiang, Mohammad Alizadeh, Harinarayanan Balakrishnan, Michael J. Cafarella, T. Kraska, S. Madden
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引用次数: 11
Abstract
The rapid development of small aerial drones has enabled numerous drone-based applications, e.g., geographic mapping, air pollution sensing, and search and rescue. To assist the development of these applications, we propose BeeCluster, a drone orchestration system that manages a fleet of drones. BeeCluster provides a virtual drone abstraction that enables developers to express a sequence of geographical sensing tasks, and determines how to map these tasks to the fleet efficiently. BeeCluster's core contribution is predictive optimization, in which an inferred model of the future tasks of the application is used to generate an optimized flight and sensing schedule for the drones that aims to minimize the total expected execution time. We built a prototype of BeeCluster and evaluated it on five real-world case studies with drones in outdoor environments, measuring speedups from 11.6% to 23.9%.
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