Abstract
In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS) system using Deep Learning (DL) algorithms and deploy it in an edge computing environment. We establish a multi-layer edge computing architecture and a distributed DL training model for the DIVS system. The DIVS system can migrate computing workloads from the network center to network edges to reduce huge network communication overhead and provide low-latency and accurate video analysis solutions. We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing. Task-level parallel and model-level parallel training methods are proposed to further accelerate the video analysis process. In addition, we propose a model parameter updating method to achieve model synchronization of the global DL model in a distributed EC environment. Moreover, a dynamic data migration approach is proposed to address the imbalance of workload and computational power of edge nodes. Experimental results showed that the EC architecture can provide elastic and scalable computing power, and the proposed DIVS system can efficiently handle video surveillance and analysis tasks.
Abstract (translated)
本文提出了一种基于深度学习（DL）算法的分布式智能视频监控系统，并将其部署在边缘计算环境中。针对DIV系统建立了多层边缘计算体系结构和分布式DL训练模型。Divs系统可以将计算工作负载从网络中心迁移到网络边缘，以减少巨大的网络通信开销，并提供低延迟和准确的视频分析解决方案。我们实现了所提出的DIV系统，并解决了并行训练、模型同步和工作负载平衡等问题。提出了任务级并行和模型级并行的训练方法，进一步加快了视频分析过程。此外，我们还提出了一种模型参数更新方法，以实现分布式EC环境下全局DL模型的模型同步。提出了一种动态数据迁移方法，解决了边缘节点工作负载和计算能力的不平衡问题。实验结果表明，该体系结构能够提供弹性和可扩展的计算能力，所提出的DIV系统能够有效地处理视频监控和分析任务。
URL
https://arxiv.org/abs/1904.06400
