An Intelligent Deep Reinforcement Learning Framework for Online Virtual Network Embedding with Graph Convolutional Networks
DOI:
https://doi.org/10.48047/hkwmt775Keywords:
Virtual Network Embedding (VNE), Software-Defined Networking (SDN), Network Function Virtualization (NFV), Resource Allocation, Quality of Service (QoS), Network OptimizationAbstract
The evolution of Virtual Network Embedding (VNE) has become a cornerstone in the optimization of resource allocation within dynamic and complex network environments. The existing research introduces InDS, a hybrid framework combining Deep Reinforcement Learning (DRL) with Graph Convolutional Networks (GCNs) to address the intricate challenges associated with VNE. InDS leverages an actor-critic model and integrates GCNs to extract rich network features, facilitating optimal decision-making for Virtual Network Requests (VNRs).
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References
Darwish, D. (Ed.). (2024). Emerging Trends in Cloud Computing Analytics, Scalability, and Service Models. 2. Barakabitze, A. A., & Walshe, R. (2022). SDN and NFV for QoE-driven multimedia services delivery: The road towards 6G and beyond networks. Computer Networks, 214, 109133.
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