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利用容器化技术构建用于边缘计算的、基于DeepStream和简单实时服务器的流服务应用程序。

The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge Computing.

作者信息

Shih Wen-Chung, Wang Zheng-Yao, Kristiani Endah, Hsieh Yi-Jun, Sung Yuan-Hsin, Li Chia-Hsin, Yang Chao-Tung

机构信息

Department of M-Commerce and Multimedia Applications, Asia University, Taichung City 413305, Taiwan.

Department of Computer Science, Tunghai University, Taichung City 407224, Taiwan.

出版信息

Sensors (Basel). 2025 Jan 5;25(1):259. doi: 10.3390/s25010259.

DOI:10.3390/s25010259
PMID:39797050
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11723310/
Abstract

This paper addresses the increasing demand for efficient and scalable streaming service applications within the context of edge computing, utilizing NVIDIA Jetson Xavier NX hardware and Docker. The study evaluates the performance of DeepStream and Simple Realtime Server, demonstrating that containerized applications can achieve performance levels comparable to traditional physical machines. The results indicate that WebRTC provides superior low-latency capabilities, achieving delays of around 5 s, while HLS typically experiences delays exceeding 10 s. Performance tests reveal that CPU usage for WebRTC can exceed 40%, which is higher than that of HLS and RTMP, while memory usage remains relatively stable across different streaming protocols. Additionally, load testing shows that the system can support multiple simultaneous connections, but performance degrades significantly with more than three devices, highlighting the limitations of the current hardware setup. Overall, the findings contribute valuable insights into building efficient edge computing architectures that support real-time video processing and streaming.

摘要

本文探讨了在边缘计算环境中对高效且可扩展的流服务应用程序日益增长的需求，利用英伟达Jetson Xavier NX硬件和Docker进行研究。该研究评估了DeepStream和简单实时服务器的性能，证明容器化应用程序可以实现与传统物理机相当的性能水平。结果表明，WebRTC具有卓越的低延迟能力，延迟约为5秒，而HLS通常延迟超过10秒。性能测试显示，WebRTC的CPU使用率可能超过40%，高于HLS和RTMP，而不同流协议下的内存使用相对稳定。此外，负载测试表明系统可以支持多个同时连接，但超过三个设备时性能会显著下降，凸显了当前硬件设置的局限性。总体而言，这些发现为构建支持实时视频处理和流传输的高效边缘计算架构提供了宝贵见解。

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