A Reinforcement Learning Approach to Intelligent Virtual Network Function Placement in NFV Ecosystems
Keywords:
Network Function Virtualization, reinforcement learning, VNF placementAbstract
Network Function Virtualization (NFV) has emerged as a transformative technology that decouples network functions from hardware, enabling greater flexibility and resource optimization in telecommunications networks. One of the key challenges in NFV ecosystems is the intelligent placement of virtual network functions (VNFs), which is crucial for optimizing network performance, minimizing latency, and efficiently utilizing resources. This paper proposes a reinforcement learning (RL)-based approach to intelligent VNF placement in NFV ecosystems. By leveraging RL, the system learns to make placement decisions that optimize performance while adapting to network dynamics in real time. The proposed approach continuously assesses and adjusts the placement of VNFs based on real-time feedback and changing network conditions, ultimately achieving more efficient and adaptive network resource allocation. The paper also discusses the benefits and challenges of implementing RL in NFV, providing case studies to illustrate its effectiveness in real-world scenarios. This framework offers a promising solution for overcoming the complexities of VNF placement and improving the overall performance of NFV systems.
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