MUVINE: Multi-Stage Virtual Network Embedding in Cloud Data Centers Using Reinforcement Learning-Based Predictions

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.

Clairvoyant: AdaBoost with Cost-Enabled Cost-Sensitive Classifier for Customer Churn Prediction

“…e first aspect of the empirical evaluation illustrated above is to determine by using three sets of iterations 10, 20, and 40 to measure the performance metrics; the falsenegative errors, misclassification cost, and mean misclassification cost are explained as follows: the misclassification cost for each set of iterations (10,20, and 40 used in the experiment) of the AdaBoostWithCost algorithm is computed from the following formula: the misclassification cost � CM[C(0, 1) × false negatives…”

“…In recent years, there have been countless applications of machine learning [ 19 ] and reinforcement learning [ 20 ] in the diversified areas such as healthcare predictions [ 21 ], cloud resource management [ 22 ], and mobile robot navigation [ 23 ]. Moreover, a significant surge is also observed in cyber frauds, as well as the corresponding model to counter them, such as credit card fraud detection, telecom churn prediction [ 2 – 5 ], and detecting rare medical diseases.…”

Virtual Network Control for Power Bills Reduction and Network Stability

“…In that study, network resource availability and demand of the VMs and physical servers are formulated, and the total required embedding time of VMs is reduced by selecting a small set of the most suitable physical servers for embedding VMs. H. K. Thakkar et al [11] presented a solution called Multi-Stage Virtual Network Embedding (MUVINE) to maximize the mean percentage of the resource utilization. The objective function is twofold: maximizing server resource (CPU, memory) utilization and minimizing the number of physical links used.…”

VNE-SDN Algorithms for Different Physical Network Environments

“…In [11], authors propose a new and efficient algorithm, which combines deep reinforcement learning with a novel neural network structure. In [12] reinforcement learning based prediction model is designed for the efficient Multi-stage Virtual Network Embedding (MUVINE) among the cloud data centers.…”

“…In SLR-VNE, we first embed the virtual node with the largest connection bandwidth, which can be obtained through Formula (12). The embedding order of the remaining virtual nodes is determined according to Formula (13).…”