Online virtual network function placement in 5G networks

The placement of network functions in 5G networks, known as the Virtual Network Function-Forwarding Graph Embedding (VNF-FGE) problem, presents challenges in resource allocation, energy efficiency, and real-time service delivery. This paper introduces two reinforcement learning methods, OGA and Hybr...

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Veröffentlicht in:	Computing Jg. 107; H. 5; S. 120
Hauptverfasser:	Esfandyari, Alborz, Zali, Zeinab, Hashemi, Massoud Reza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Vienna Springer Vienna 01.05.2025 Springer Nature B.V
Schlagworte:	5G mobile communication Artificial Intelligence Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Energy consumption Genetic algorithms Information Systems Applications (incl.Internet) Linear programming Optimization Performance evaluation Placement Real time Regular Paper Resource allocation Software Engineering Virtual networks Wireless networks Multi-access edge computing 68 Genetic algorithm 49 Optimal placement Reinforcement learning 5G wireless network technology Network function virtualization 94
ISSN:	0010-485X, 1436-5057
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Abstract	The placement of network functions in 5G networks, known as the Virtual Network Function-Forwarding Graph Embedding (VNF-FGE) problem, presents challenges in resource allocation, energy efficiency, and real-time service delivery. This paper introduces two reinforcement learning methods, OGA and Hybrid-OGA, modeling the VNF-FGE problem as a binary linear programming problem. OGA uses a seq2seq model with actor-critic reinforcement learning for optimal service chain placement, while Hybrid-OGA incorporates a genetic algorithm for refining blocked services placement. These methods address critical issues in 5G network function placement, optimizing VNF placement to minimize energy consumption while maintaining service performance and dependability. We evaluate the methods against First-Fit and CPLEX optimization tools, showing competitive performance in response time, accepted ratio, blocked services, and objective function minimization. Our methods reduce the objective function by 15–40% and improve the accepted ratio by 5–20% compared to CPLEX. With increased request rates, our methods show a 7% decrease in the accepted ratio, while others show a 14–20% decrease. Additionally, our methods increase the objective function by 20–33%, compared to a 48% increase in methods with higher service block rates.
AbstractList	The placement of network functions in 5G networks, known as the Virtual Network Function-Forwarding Graph Embedding (VNF-FGE) problem, presents challenges in resource allocation, energy efficiency, and real-time service delivery. This paper introduces two reinforcement learning methods, OGA and Hybrid-OGA, modeling the VNF-FGE problem as a binary linear programming problem. OGA uses a seq2seq model with actor-critic reinforcement learning for optimal service chain placement, while Hybrid-OGA incorporates a genetic algorithm for refining blocked services placement. These methods address critical issues in 5G network function placement, optimizing VNF placement to minimize energy consumption while maintaining service performance and dependability. We evaluate the methods against First-Fit and CPLEX optimization tools, showing competitive performance in response time, accepted ratio, blocked services, and objective function minimization. Our methods reduce the objective function by 15–40% and improve the accepted ratio by 5–20% compared to CPLEX. With increased request rates, our methods show a 7% decrease in the accepted ratio, while others show a 14–20% decrease. Additionally, our methods increase the objective function by 20–33%, compared to a 48% increase in methods with higher service block rates.
ArticleNumber	120
Author	Zali, Zeinab Hashemi, Massoud Reza Esfandyari, Alborz
Author_xml	– sequence: 1 givenname: Alborz surname: Esfandyari fullname: Esfandyari, Alborz organization: Department of Electrical and Computer Engineering, Isfahan University of Technology – sequence: 2 givenname: Zeinab surname: Zali fullname: Zali, Zeinab email: zali@iut.ac.ir organization: Department of Electrical and Computer Engineering, Isfahan University of Technology – sequence: 3 givenname: Massoud Reza surname: Hashemi fullname: Hashemi, Massoud Reza organization: Department of Electrical and Computer Engineering, Isfahan University of Technology
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Snippet	The placement of network functions in 5G networks, known as the Virtual Network Function-Forwarding Graph Embedding (VNF-FGE) problem, presents challenges in...
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SubjectTerms	5G mobile communication Artificial Intelligence Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Energy consumption Genetic algorithms Information Systems Applications (incl.Internet) Linear programming Optimization Performance evaluation Placement Real time Regular Paper Resource allocation Software Engineering Virtual networks Wireless networks
Title	Online virtual network function placement in 5G networks
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