RNN‐EdgeQL: An auto‐scaling and placement approach for SFC-Reference-Cited by-同舟云学术

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RNN‐EdgeQL: An auto‐scaling and placement approach for SFC

Published:2022-09-09 Issue: Volume: Page:
ISSN:1055-7148
Container-title:International Journal of Network Management
language:en
Short-container-title:Int J Network Mgmt

Author:

Pandey Suman¹^ORCID,Choi Minji²^ORCID,Yoo Jae‐Hyoung³,Hong James Won‐Ki³

Affiliation:

1. School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju South Korea

2. Graduate School of Artificial Intelligence Pohang University of Science and Technology Pohang South Korea

3. Department of Computer Science and Engineering Pohang University of Science and Technology Pohang South Korea

Funder

Iran Telecommunication Research Center

Publisher

Wiley

Subject

Computer Networks and Communications,Computer Science Applications

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/nem.2213

Reference34 articles.

1. ErsueM.ETSI NFV management and orchestration‐An overview. Presentation at the IETF 88;2013.

2. A network intelligence architecture for efficient VNF lifecycle management;Lange S;IEEE Trans Netw Serv Manag,2020

3. ChoK Van MerriënboerB BahdanauD BengioY.On the properties of neural machine translation: Encoder‐decoder approaches. arXiv:1409.1259;2014.

4. Bidirectional recurrent neural networks

5. Q-learning

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