Abstract

The emerging 5G applications and the connectivity of billions of devices have driven the investigation of multi-domain heterogeneous converged optical networks. To support emerging applications with their diverse quality of service requirements, network slicing has been proposed as a promising technology. Network virtualization is an enabler for network slicing, where the physical network can be partitioned into different configurable slices in the multi-domain heterogeneous converged optical networks. An efficient resource allocation mechanism for multiple virtual networks in network virtualization is one of the main challenges referred as virtual network embedding (VNE). This paper is a survey on the state-of-the-art works for the VNE problem towards multi-domain heterogeneous converged optical networks, providing the discussion on future research issues and challenges. In this paper, we describe VNE in multi-domain heterogeneous converged optical networks with enabling network orchestration technologies and analyze the literature about VNE algorithms with various network considerations for each network domain. The basic VNE problem with various motivations and performance metrics for general scenarios is discussed. A VNE algorithm taxonomy is presented and discussed by classifying the major VNE algorithms into three categories according to existing literature. We analyze and compare the attributes of algorithms such as node and link embedding methods, objectives, and network architecture, which can give a selection or baseline for future work of VNE. Finally, we explore some broader perspectives in future research issues and challenges on 5G scenario, field trail deployment, and machine learning-based algorithms.

Highlights

  • The exponential growth of the emerging of dynamic applications and the billions of devices in the Internet of Things (IoT) with sensing, computing, and communication capabilities have driven the investigation of network architecture

  • The virtual network embedding (VNE) problem is divided into two sub-problems as described in Section 3, we review the methods for Virtual Node Embedding (VNoE) and Virtual Link Embedding (VLiE)

  • This paper has presented a survey of existing works on the VNE problem towards multi-domain heterogeneous converged optical network, which have focused on the resource allocation optimization of multiple virtual networks coexisting and sharing resource in substrate networks

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Summary

Introduction

The exponential growth of the emerging of dynamic applications and the billions of devices in the Internet of Things (IoT) with sensing, computing, and communication capabilities have driven the investigation of network architecture. IoT services in multi-domain heterogeneous converged optical networks. Many existing works have focused on specific domain network architecture of heterogeneous converged optical networks for the VNE approaches, such as wireless network, fiber-wireless (FiWi) access network, and optical data center network (ODCN). In [16], the authors have focused on algorithmic aspects for VNE for cloud networks These surveys have not focused on VNE for multi-domain heterogeneous converged optical networks and the future research issues on implementation and intelligent algorithms. Multi-domain heterogeneous converged optical network architecture has been described and the differences among them are discussed, e.g., radio resource for wireless channel, spectrum characteristics for optical network, and various substrate nodes. We review the VNE in wireless network, FiWi access network, and optical network single domain in the following

Key Enabling Technologies
Wireless Network
Fiber-Wireless Access Network
Optical Network
Virtual Network Embedding Problem
Substrate Network
Virtual Network
Virtual Network Embedding
Profit
Acceptance Ratio
Resource Utilization
Latency
Energy Efficiency
Survivability
Traffic Prediction
VNE Algorithms Taxonomy
Two-Stage VNE Algorithms
Virtual Node Embedding
Virtual Link Embedding
Coordinated VNE Algorithms
Machine Learning Based VNE Algorithms
Issues and Challenges
Field Trial Deployment
Machine Learning Based Management Algorithm
Conclusions

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