Dynamic Joint VNF Forwarding Graph Composition and Embedding: A Deep Reinforcement Learning Framework-Reference-Cited by-同舟云学术

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Dynamic Joint VNF Forwarding Graph Composition and Embedding: A Deep Reinforcement Learning Framework

Published:2023-12 Issue:4 Volume:20 Page:4615-4633
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Malektaji Sepideh¹^ORCID,Rayani Marsa¹,Ebrahimzadeh Amin¹^ORCID,Raee Vahid Maleki¹^ORCID,Elbiaze Halima²^ORCID,Glitho Roch H.³^ORCID

Affiliation:

1. Concordia Institute for Information System Engineering, Concordia University, Montreal, Canada

2. Department of Computer Science, Université du Québec à Montréal, Montreal, Canada

3. Computer Science Program, University of Western Cape, Capetown, South Africa

Funder

Québec FQRNT in the context of SCORIN

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/4275028/10355751/10073577.pdf?arnumber=10073577

Reference61 articles.

1. A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems

2. Toward 6G

3. Resource Allocation in NFV: A Comprehensive Survey

4. Online Joint VNF Chain Composition and Embedding for 5G Networks

5. Availability-Aware Service Chain Composition and Mapping in NFV-Enabled Networks

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