CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing

Yongshuai Liu, Jiaxin Ding, Zhi Li Zhang, Xin Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

As mobile networks proliferate, we are experiencing a strong diversification of services, which requires greater flexibility from the existing network. Network slicing is proposed as a promising solution for resource utilization in 5G and future networks to address this dire need. In network slicing, dynamic resource orchestration and network slice management are crucial for maximizing resource utilization. Unfortunately, this process is too complex for traditional approaches to be effective due to a lack of accurate models and dynamic hidden structures. We formulate the problem as a Constrained Markov Decision Process (CMDP) without knowing models and hidden structures. Additionally, we propose to solve the problem using CLARA, a Constrained reinforcement LeArning based Resource Allocation algorithm. In particular, we analyze cumulative and instantaneous constraints using adaptive interior-point policy optimization and projection layer, respectively. Evaluations show that CLARA clearly outperforms baselines in resource allocation with service demand guarantees.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
EditorsYixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1427-1437
Number of pages11
ISBN (Electronic)9781665439022
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States
Duration: Dec 15 2021Dec 18 2021

Publication series

NameProceedings - 2021 IEEE International Conference on Big Data, Big Data 2021

Conference

Conference2021 IEEE International Conference on Big Data, Big Data 2021
Country/TerritoryUnited States
CityVirtual, Online
Period12/15/2112/18/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT The work was partially supported by NSF through grants IIS-1838207, CNS 1901218, OIA-2040680, OIA-2134901 and USDA-020-67021-32855. J. Ding would like to acknowledge supports from Shanghai Sailing Program 20YF1421300.

Publisher Copyright:
© 2021 IEEE.

Keywords

  • 5G
  • Constraints
  • Deep Reinforcement Learning
  • Network Slicing
  • Resource Allocation

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