Deep reinforcement learning based edge computing for video processing

In many of 5G applications, end devices with lack of computing power often need to carry out heavy computations involving multimedia data. Edge computing has emerged as a promising solution to circumvent scarce resources at end devices, with moderate delays compared to cloud computing. In this work,...

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Bibliographic Details
Published inICT express Vol. 9; no. 3; pp. 433 - 438
Main Authors Han, Seung-Yeop, Lee, Hyang-Won
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.06.2023
Elsevier
한국통신학회
Subjects
Edge computing
Reinforcement learning
Video processing
전자/정보통신공학
Video processing
Reinforcement learning
Edge computing
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Summary:In many of 5G applications, end devices with lack of computing power often need to carry out heavy computations involving multimedia data. Edge computing has emerged as a promising solution to circumvent scarce resources at end devices, with moderate delays compared to cloud computing. In this work, we study the problem of offloading video processing tasks to edge servers. To this end, we develop a deep reinforcement learning based method for selecting either local or edge server to process video frames. We demonstrate the performance of our method through experiments with video frame transform tasks.
ISSN:2405-9595
2405-9595
DOI:10.1016/j.icte.2022.05.001