Network Resource Allocation Strategy Based on Deep Reinforcement Learning

TL;DR

This paper introduces three deep reinforcement learning-based algorithms for virtual network embedding, significantly improving resource allocation efficiency and solution optimality in virtualized networks.

Contribution

It presents three novel DRL algorithms addressing local optima, substrate network representation, and resource dynamics in VNE, advancing network virtualization techniques.

Findings

Algorithms outperform existing methods in experiments.

Enhanced resource utilization and embedding success rate.

Improved adaptability to network resource changes.

Abstract

The traditional Internet has encountered a bottleneck in allocating network resources for emerging technology needs. Network virtualization (NV) technology as a future network architecture, the virtual network embedding (VNE) algorithm it supports shows great potential in solving resource allocation problems. Combined with the efficient machine learning (ML) algorithm, a neural network model close to the substrate network environment is constructed to train the reinforcement learning agent. This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that the mapping result of existing heuristic algorithm is easy to converge to the local optimal solution. For the problem that the existing VNE algorithm based on ML often ignores the importance of substrate network representation and training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE) is proposed. In view of the problem that the existing VNE algorithm often ignores the underlying resource changes between virtual network requests, a DRL VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed. Experimental results show that the above algorithm is superior to other algorithms.