Joint Virtual Network Function Deployment and Scheduling via Heuristics and Deep Reinforcement Learning
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/12/E106.B_2023EBP3039/_pdf
Reference32 articles.
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2. [2] M.G. Khan, J. Taheri, A. Kassler, and M. Darula, “Automated analysis and profiling of virtual network functions: The NFV-inspector approach,” 2018 IEEE Conf. Netw. Func. Virtual. Soft. Def. Netw. (NFV-SDN), pp.1-2, 2018. 10.1109/nfv-sdn.2018.8725697
3. [3] Z. Zhang, F. He, and E. Oki, “Dynamic VNF scheduling: A deep reinforcement learning approach,” IEICE Trans. Commun., vol.E106-B, no.7, pp.557-570, July 2023. 10.1587/transcom.2022ebp3160
4. [4] N. Promwongsa, A. Ebrahimzadeh, R.H. Glitho, and N. Crespi, “Joint VNF placement and scheduling for latency-sensitive services,” IEEE Trans. Netw. Sci. Eng., vol.9, no.4, pp.2432-2449, 2022. 10.1109/tnse.2022.3163927
5. [5] Y. Zhang, F. He, T. Sato, and E. Oki, “Network service scheduling with resource sharing and preemption,” IEEE Trans. Netw. Service Manage., vol.17, no.2, pp.764-778, 2020. 10.1109/tnsm.2019.2956949
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