MUVINE: Multi-stage Virtual Network Embedding in Cloud Data Centers using Reinforcement Learning based Predictions

TL;DR

This paper introduces MUVINE, a reinforcement learning-based multi-stage virtual network embedding scheme that improves resource utilization and efficiency in cloud data centers by predicting future demands.

Contribution

The paper presents a novel reinforcement learning approach for multi-stage virtual network embedding, addressing the limitations of traditional methods by predicting future resource demands.

Findings

MUVINE outperforms existing schemes in simulations.

Reinforcement learning effectively predicts resource demands.

Enhanced resource utilization in cloud data centers.

Abstract

The recent advances in virtualization technology have enabled the sharing of computing and networking resources of cloud data centers among multiple users. Virtual Network Embedding (VNE) is highly important and is an integral part of the cloud resource management. The lack of historical knowledge on cloud functioning and inability to foresee the future resource demand are two fundamental shortcomings of the traditional VNE approaches. The consequence of those shortcomings is the inefficient embedding of virtual resources on Substrate Nodes (SNs). On the contrary, application of Artificial Intelligence (AI) in VNE is still in the premature stage and needs further investigation. Considering the underlying complexity of VNE that includes numerous parameters, intelligent solutions are required to utilize the cloud resources efficiently via careful selection of appropriate SNs for the VNE. In this paper, Reinforcement Learning based prediction model is designed for the efficient Multi-stage Virtual Network Embedding (MUVINE) among the cloud data centers. The proposed MUVINE scheme is extensively simulated and evaluated against the recent state-of-the-art schemes. The simulation outcomes show that the proposed MUVINE scheme consistently outperforms over the existing schemes and provides the promising results.