2018/05/04 by Justin Hu, Ariana Bruno, Hu, Justin +16 · 2 citations
Computer Science · #Cloud Computing and Resource Management #Distributed #Distributed and Parallel Computing Systems #Distributed systems and fault tolerance #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1805.01786
openalex publication_date 2018/05/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Large swarms of autonomous devices are increasing in size and importance. When it comes to controlling the devices of large-scale swarms there are two main lines of thought. Centralized control, where all decisions - and often compute - happen in a centralized back-end cloud system, and distributed control, where edge devices are responsible for selecting and executing tasks with minimal or zero help from a centralized entity. In this work we aim to quantify the trade-offs between the two approaches with respect to task assignment quality, latency, and reliability. We do so first on a local swarm of 12 programmable drones with a 10-server cluster as the backend cloud, and then using a validated simulator to study the tail at scale effects of swarm coordination control. We conclude that although centralized control almost always outperforms distributed in the quality of its decisions, it faces significant scalability limitations, and we provide a list of system challenges that need to be addressed for centralized control to scale.