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2023 | OriginalPaper | Chapter

Resource Management in Cloud Computing Using Deep Reinforcement Learning: A Survey

Authors : Yuxin Feng, Feiyang Liu

Published in: Proceedings of the 10th Chinese Society of Aeronautics and Astronautics Youth Forum

Publisher: Springer Nature Singapore

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Abstract

Next generation aircrafts will not only require high-performance and intelligent computing capabilities, but also a fast design-developing-integration-update time cycle. Cloud computing technology provides a platform with large amounts of hardware resources and software services, making applications development and deployment much more convenient. Thus, airborne cloud computing becomes an important design methodology for next generation avionics system. However, the dynamic and uncertain cloud environment makes efficient resource management very complicated. Due to the characteristics of dynamic autonomous decision-making, deep reinforcement learning has become a promising resource scheduling algorithm. This paper firstly analyzes the requirements of airborne cloud computing systems, then studies the basic theory of cloud resources management and scheduling strategies. The resource management algorithms based on deep reinforcement learning (DRL), some common DRL models, experimental platforms, and evaluation parameters are introduced in details. Finally, some critical problems and challenges in the design of DRL-based resource management algorithm are summarized. This paper can provide some technique supports for the airborne cloud computing system.

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Metadata
Title
Resource Management in Cloud Computing Using Deep Reinforcement Learning: A Survey
Authors
Yuxin Feng
Feiyang Liu
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of the 10th Chinese Society of Aeronautics and Astronautics Youth Forum

Print ISBN: 978-981-19-7651-3

Electronic ISBN: 978-981-19-7652-0

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-7652-0_56

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