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<p>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.</p>

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Online virtual network function placement in 5G networks

  • 期刊论文

  • 发表日期：

    2025年4月23日

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  • Alborz Esfandyari,
  • Zeinab Zali,
  • Massoud Reza Hashemi

摘要

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.