BeeCluster: Drone Orchestration via Predictive Optimization

Songtao He, Favyen Bastani, Arjun Balasingam, Karthik Gopalakrishnan, Ziwen Jiang, Mohammad Alizadeh, Hari Balakrishnan, Michael Cafarella, Tim Kraska, Samuel Madden
18th International Conference on Mobile Systems, Applications, and Services (MobiSys), Toronto, Canada, June 2020

The rapid development of small aerial drones has enabled numerous drone-based applications, eg, 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.

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Bibtex Entry:

@inproceedings{he2020beecluster,
   author =       "Songtao He and Favyen Bastani and Arjun Balasingam and Karthik Gopalakrishnan and Ziwen Jiang and Mohammad Alizadeh and Hari Balakrishnan and Michael Cafarella and Tim Kraska and Samuel Madden",
   title =        "{BeeCluster: Drone Orchestration via Predictive Optimization}",
   booktitle =    {18th International Conference on Mobile Systems, Applications, and Services (MobiSys)},
   year =         {2020},
   month =        {June},
   address =      {Toronto, Canada}
}