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| Title | Coedge: A Cooperative Edge System For Distributed Real-Time Deep Learning Tasks |
|---|---|
| ID_Doc | 14625 |
| Authors | Jiang Z.; Ling N.; Huang X.; Shi S.; Wu C.; Zhao X.; Yan Z.; Xing G. |
| Year | 2023 |
| Published | IPSN 2023 - Proceedings of the 2023 22nd International Conference on Information Processing in Sensor Networks |
| DOI | http://dx.doi.org/10.1145/3583120.3586955 |
| Abstract | Recent years have witnessed the emergence of a new class of cooperative edge systems in which a large number of edge nodes can collaborate through local peer-to-peer connectivity. In this paper, we propose CoEdge, a novel cooperative edge system that can support concurrent data/compute-intensive deep learning (DL) models for distributed real-time applications such as city-scale traffic monitoring and autonomous driving. First, CoEdge includes a hierarchical DL task scheduling framework that dispatches DL tasks to edge nodes based on their computational profiles, communication overhead, and real-time requirements. Second, CoEdge can dramatically increase the execution efficiency of DL models by batching sensor data and aggregating the inferences of the same model. Finally, we propose a new edge containerization approach that enables an edge node to execute concurrent DL tasks by partitioning the CPU and GPU workloads into different containers. We extensively evaluate CoEdge on a self-deployed smart lamppost testbed on a university campus. Our results show that CoEdge can achieve up to reduction on deadline missing rate compared to baselines. © 2023 ACM. |
| Author Keywords | Distributed Deep Learning System; Edge Computing; Edge Containerization; Real-time Scheduling; Smart City |
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