Deep Reinforcement Learning for Resource Management in Network Function Virtualization

Abstract Network Function Virtualization (NFV) has revolutionized the networking land- scape by enabling the flexible deployment of network services through software. However, efficient resource management remains a critical challenge in NFV envi- ronments due to the dynamic nature of network conditions and workloads. This research paper proposes a novel approach to address this challenge by employing deep reinforcement learning techniques for resource management in NFV. The paper presents a comprehensive framework that combines deep reinforcement learning principles with NFV’s resource allocation problem. By leveraging this approach, intelligent and adaptive resource allocation strategies are developed, enhancing NFV performance, reducing operational costs, and optimizing resource utilization. Through rigorous experimentation and analysis, the paper evaluates the proposed framework’s effectiveness, comparing it with existing methods and showcasing its potential to transform NFV resource management.