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This paper presents a novel energy-efficient online coordinated virtual network embedding framework called PPO-VNE, which utilizes deep reinforcement learning to address the NP-hard virtual network embedding (VNE) problem. By employing the Proximal Policy Optimization algorithm and a hybrid feature extraction approach, PPO-VNE effectively coordinates node and link mapping while optimizing multiple objectives such as acceptance rate, revenue, and energy consumption. Experimental results demonstrate that PPO-

Author

keerthan

Language

EN