Continuous Learning-Based Task Demand Understanding and Scheduling Method for Video Internet of Things

Xu Dongzhu; Zhou Anfu; Ma Huadong; Zhang Yuan

doi:10.7544/issn1000-1239.202440403

Journal of Computer Research and Development > 2024 > 61(11): 2793-2805. > DOI: 10.7544/issn1000-1239.202440403 CSTR: 32373.14.issn1000-1239.202440403

Xu Dongzhu, Zhou Anfu, Ma Huadong, Zhang Yuan. Continuous Learning-Based Task Demand Understanding and Scheduling Method for Video Internet of Things[J]. Journal of Computer Research and Development, 2024, 61(11): 2793-2805. DOI: 10.7544/issn1000-1239.202440403

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Continuous Learning-Based Task Demand Understanding and Scheduling Method for Video Internet of Things

Xu Dongzhu^{1, 2,},
Zhou Anfu^{1, 2},
Ma Huadong^{1, 2, ,},
Zhang Yuan³

1.
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876
2.
State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications), Beijing 100876
3.
China Telecommunications Corporation, Shanghai 200125

Funds: This work was supported by the Innovation Research Group Project of the National Natural Science Foundation of China (61921003) the National Natural Science Foundation of China for Young Scientists (6240072128), and the China National Postdoctoral Program for Innovative Talents (BX20240046).

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Author Bio:
Xu Dongzhu: born in 1996. Postdoc. His main research interests include VIoT and time-sensitive networking

Zhou Anfu: born in 1981. PhD, professor. His main research interests include IoT and mobile computing

Ma Huadong: born in 1964. PhD, professor. His main research interests include IoT, sensor network, and multimedia computing

Zhang Yuan: born in 1986. Master. Her main research interests include AI and IoT, and image/video coding
Received Date: May 30, 2024
Revised Date: August 04, 2024
Available Online: August 13, 2024

Graphical Abstract

Abstract

Abstract

Efficient scheduling between cloud-network resources and video tasks is crucial for the performance of video Internet of things (VIoT) applications. However, the current scheduling algorithms used in operational VIoT systems are insufficiently adaptable to differentiated task demands and highly dynamic changes in cloud-network resources, resulting in poor performance of VIoT applications. To overcome the aforementioned problem, we propose a continuous learning-based task demand understanding and scheduling method for VIoT (CLTUS) Unlike traditional heuristic or machine learning-driven scheduling algorithms, CLTUS integrates the continuous learning into the matching between cloud-network resources and video task demands. Specifically, it first employs a continuous learning framework to accurately comprehend various video task demands. Subsequently, based on the dependency relationships among video tasks, it achieves an optimal match between tasks and servers, thereby refining the scheduling of cloud-network resources. Finally, the proposed method is deployed on a software-defined VIoT experimental platform. Compared with conventional methods, CLTUS not only improves the average processing efficiency of video tasks by 127.73% but also increases the balanced utilization rate of cloud-network resources to 67.2% on average, effectively improving the performance of VIoT applications.
- video Internet of things,
- continuous learning,
- task demand understanding,
- resource scheduling,
- operational network measurement

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References (25)

References

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