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Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings

Research Article

Service-Aware Virtual Network Function Migration Based on Deep Reinforcement Learning

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  • @INPROCEEDINGS{10.1007/978-3-030-99200-2_36,
        author={Zeming Li and Ziyu Liu and Chengchao Liang and Zhanjun Liu},
        title={Service-Aware Virtual Network Function Migration Based on Deep Reinforcement Learning},
        proceedings={Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings},
        proceedings_a={CHINACOM},
        year={2022},
        month={4},
        keywords={Network Function Virtualization Virtual Network Functions Markov decision process Migration Deep reinforcement learning},
        doi={10.1007/978-3-030-99200-2_36}
    }
    
  • Zeming Li
    Ziyu Liu
    Chengchao Liang
    Zhanjun Liu
    Year: 2022
    Service-Aware Virtual Network Function Migration Based on Deep Reinforcement Learning
    CHINACOM
    Springer
    DOI: 10.1007/978-3-030-99200-2_36
Zeming Li1,*, Ziyu Liu1, Chengchao Liang1, Zhanjun Liu1
  • 1: School of Communication and Information Engineering
*Contact email: 1964625523@qq.com

Abstract

Network Function Virtualization (NFV) aims to provide a way to build agile and flexible networks by building a new paradigm of provisioning network services where network functions are virtualized as Virtual Network Functions (VNFs). Network services are implemented by service function chains, which are formed by a series of VNFs with a specific traversal order. VNF migration is a critical procedure to reconfigure VNFs for providing better network services. However, the migration of VNFs for dynamic service requests is a key challenge. Most VNF migration works mainly focused on static threshold trigger mechanism which will cause frequent migration. Therefore, we propose a novel mechanism to solve the issue in this paper. With the objective of minimizing migration overhead, a stochastic optimization problem based on Markov decision process is formulated. Moreover, we prove the NP-hardness of the problem and propose a service-aware VNF migration scheme based on deep reinforcement learning. Extensive simulations are conducted that the proposed scheme can effectively avoid frequent migration and reduce the migration overhead.

Keywords
Network Function Virtualization Virtual Network Functions Markov decision process Migration Deep reinforcement learning
Published
2022-04-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-99200-2_36
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