Blocking Island Paradigm Enhanced Intelligent Coordinated Virtual Network Embedding Based on Deep Reinforcement Learning

As an efficient technique for resource sharing in data centers, network virtualization enables resource multiplexing by allowing multiple heterogeneous virtual networks (VNs) to simultaneously coexist on the shared substrate infrastructure. How to effectively embed the VNs onto the substrate network is known as the virtual network embedding (VNE) problem. However, as an NP-hard problem, the VNE problem-solving suffers a high computation complexity. Artificial Intelligence (AI) provides a promising way to alleviate these issues. However, the existing AI-based works still cannot fully and efficiently leverage substrate network information to formulate embedding policies. To this end, in this paper we propose a novel deep reinforcement learning (DRL) based coordinated VNE algorithm, called Intelligent Coordinated Embedding (ICE). To reduce the computation complexity, ICE adopts an efficient resource abstraction model, Blocking Island (BI), which greatly reduces the search space. With the benefit of DRL and BI, ICE can efficiently adjust embedding strategies according to the environment states, aiming to maximize resource utilization and overall revenue while minimizing the embedding cost. The experimental results prove that ICE outperforms both the traditional non-DRL-based approach and the state-of-the-art DRL-based approach.