Dynamic VNF Scheduling: A Deep Reinforcement Learning Approach
Author:
Affiliation:
1. Kyoto University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Software
Link
https://www.jstage.jst.go.jp/article/transcom/E106.B/7/E106.B_2022EBP3160/_pdf
Reference21 articles.
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2. [2] S.I. Kim and H.S. Kim, “A VNF placement method based on VNF characteristics,” 2021 Int. Conf. Inf. Netw. (ICOIN 2021), pp.864-869, 2021. 10.1109/icoin50884.2021.9334022
3. [3] Z. Wang, J. Zhang, H. Wei, and T. Huang, “Hieff: Enabling efficient VNF clusters by coordinating VNF scaling and flow scheduling,” 2020 IEEE 39th Int. Perform. Comput. Commun. Conf. (IPCCC 2020), pp.1-8, 2020. 10.1109/ipccc50635.2020.9391534
4. [4] X. Zhao, X. Jia, and Y. Hua, “An efficient VNF deployment algorithm for SFC scaling-out based on the proposed scaling management mechanism,” 2020 Inf. Commun. Technol. Conf. (ICTC 2020), pp.166-170, 2020. 10.1109/ictc49638.2020.9123252
5. [5] J. Li, W. Shi, N. Zhang, and X. Shen, “Delay-aware VNF scheduling: A reinforcement learning approach with variable action set,” IEEE Trans. Cogn. Commun. Netw., vol.7, no.1, pp.304-318, 2021. 10.1109/tccn.2020.2988908
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