The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge Computing
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 container...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/259 |
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| _version_ | 1841548917529903104 |
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| author | Wen-Chung Shih Zheng-Yao Wang Endah Kristiani Yi-Jun Hsieh Yuan-Hsin Sung Chia-Hsin Li Chao-Tung Yang |
| author_facet | Wen-Chung Shih Zheng-Yao Wang Endah Kristiani Yi-Jun Hsieh Yuan-Hsin Sung Chia-Hsin Li Chao-Tung Yang |
| author_sort | Wen-Chung Shih |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-501d1fc5c1d849629b56fe38cdd5e385 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-501d1fc5c1d849629b56fe38cdd5e3852025-01-10T13:21:23ZengMDPI AGSensors1424-82202025-01-0125125910.3390/s25010259The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge ComputingWen-Chung Shih0Zheng-Yao Wang1Endah Kristiani2Yi-Jun Hsieh3Yuan-Hsin Sung4Chia-Hsin Li5Chao-Tung Yang6Department of M-Commerce and Multimedia Applications, Asia University, Taichung City 413305, TaiwanDepartment of Computer Science, Tunghai University, Taichung City 407224, TaiwanDepartment of Computer Science, Tunghai University, Taichung City 407224, TaiwanDepartment of Computer Science, Tunghai University, Taichung City 407224, TaiwanDepartment of Computer Science, Tunghai University, Taichung City 407224, TaiwaniAMBITION TECHNOLOGY Co., Ltd., 3F., No. 159-1, Sec. 1, Zhongxing Rd., Dali District, Taichung City 412031, TaiwanDepartment of Computer Science, Tunghai University, Taichung City 407224, TaiwanThis 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.https://www.mdpi.com/1424-8220/25/1/259Dockeredge computingsimple realtime serverdeepstreamJetson Xavier NX |
| spellingShingle | Wen-Chung Shih Zheng-Yao Wang Endah Kristiani Yi-Jun Hsieh Yuan-Hsin Sung Chia-Hsin Li Chao-Tung Yang The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge Computing Sensors Docker edge computing simple realtime server deepstream Jetson Xavier NX |
| title | The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge Computing |
| title_full | The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge Computing |
| title_fullStr | The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge Computing |
| title_full_unstemmed | The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge Computing |
| title_short | The Construction of a Stream Service Application with DeepStream and Simple Realtime Server Using Containerization for Edge Computing |
| title_sort | construction of a stream service application with deepstream and simple realtime server using containerization for edge computing |
| topic | Docker edge computing simple realtime server deepstream Jetson Xavier NX |
| url | https://www.mdpi.com/1424-8220/25/1/259 |
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