Virtual Network Embedding Algorithm Research Articles

DROI: Energy-efficient virtual network embedding algorithm based on dynamic regions of interest

Network virtualization is an important technology for the next generation of the internet. Moreover, energy-efficient virtual network embedding has gradually become a research hotspot due to the increase in energy awareness. Given the lack of consideration of substrate and virtual network topology information in energy-efficient virtual network embedding, this work applies spectral clustering based on field theory to extract substrate network features. Dynamic regions of interest are also established to find embedding areas with energy-saving potential for virtual networks. The largest matching area of a virtual network on a substrate network is identified by considering virtual network topology information. Experimental results show that the proposed approach greatly improves energy-saving relative to existing algorithms.

Intelligent Latency-Aware Virtual Network Embedding for Industrial Wireless Networks

The growing popularity of industrial wireless networks (IWNs) is driven by various applications with stringent timeliness requests. However, the ossification, deep-rooted in the one-application one-network architecture of traditional IWNs, impedes the evolution of IWNs toward smart factory. As a solution, the slice-based network virtualization (NV) breaks the tight coupling between applications and network infrastructure, and thus provides a more flexible and scalable IWN architecture. The application of NV relies on the algorithms that instantiate multiple virtual networks (VNs) on a substrate infrastructure, known as VN embedding (VNE). However, existing VNE algorithms are not necessarily optimal for IWNs due to the absence of QoS-compliant capacity. To this end, so called iVNE, an intelligent latency-aware VNE scheme, is proposed to provide deadline guarantee for various industrial VNs (IVNs), which involves both static embedding and dynamic forwarding. In the static stage, an anypath embedding algorithm is introduced for the new arrival of IVNs so that their resource demands and deadlines can be satisfied with coarse grain. Then, a dynamic anypath forwarding method is incorporated into iVNE to offer intelligent latency sensing via deep ${Q}$ -learning, and thus forwarding adjustments can be made timely to address the dynamic changes of link quality and network workload. The simulation results are provided to demonstrate the learning efficiency as well as the ability of load-balancing through responsive forwarding under dynamic environment.

Mapping strategy for virtual networks in one stage

In the area of network virtualisation, virtual network embedding (VNE) refers to the resource allocation problem. In the literature, researchers have proposed multiple VNE algorithms. These algorithms have the goal of accommodating as many requested virtual networks (VNs) as possible. However, most of prior embedding algorithms belong to the two-stage (separated node and link embeddings) mapping algorithm category. Certain embedding algorithms embed each VN in one mapping stage by using mixed integer linear programming approach or graph theory, having very high computation time. There is a lack of heuristic algorithms, enabling to embed nodes and links per VN in one mapping stage. In addition, each requested VN embedding needs to be completed in polynomial time so as to be promoted to future dynamic VN service application and real-time VNs embedding. Based on these backgrounds, the authors propose a novel real-time and one-stage heuristic mapping algorithm (VNE-RTOS). Numerical evaluations are conducted to strengthen that VNE-RTOS earns more embedding revenues by 8% over typical two-stage heuristic embedding algorithms (e.g. VNE-TAGRD) while achieving the same substrate resource utilisation.

A Q-learning-based approach for virtual network embedding in data center

Virtual network embedding (VNE) refers to allocating reasonable substrate network resources for virtual network (VN) requests that include computing resources and network resources, so as to obtain optimal income from leasing virtual resources. Such a way of providing virtual resources is the key technology of cloud computing and can greatly save the operating cost of enterprises and provide flexibility of application deployment. However, the existing VNE algorithms are mostly oriented to traditional stochastic network topologies. Due to the high connectivity and server density of data centers and the complexity of the user’s resource requirements, the traditional VNE algorithms suffer from low resource utilization rate and revenues in the VNE on the data centers. Different from the existing algorithms which are often based on heuristic algorithms, this paper proposes a VNE algorithm for data center topology based on the Q-learning algorithm which is a typical reinforcement learning method. The algorithm an agent for each VN designs a reward function related to the effect of virtual link embedding, which is used to update the Q-matrix through unsupervised learning process. Then, the agent can find the optimal embedding strategy based on the Q-table from each learning. Simulation results demonstrate that the proposed algorithm can improve the resource utilization ratio and obtain a better revenue/cost ratio of the substrate network compared with the traditional heuristic algorithms.

VNE-TD: A virtual network embedding algorithm based on temporal-difference learning

Recently, network virtualization is considered as a promising solution for the future Internet which can help to overcome the resistance of the current Internet to fundamental changes. The problem of embedding Virtual Networks (VN) in a Substrate Network (SN) is the main resource allocation challenge in network virtualization. The major challenge of the Virtual Network Embedding (VNE) problem lies in the contradiction between making online embedding decisions and pursuing a long-term objective. Most previous works resort to balancing the SN workload with various methods to deal with this contradiction. Rather than passive balancing, we try to overcome it by learning actively and making online decisions based on previous experiences. In this article, we model the VNE problem as Markov Decision Process (MDP) and develop a neural network to approximate the value function of VNE states. Further, a VNE algorithm based on Temporal-Difference Learning (one kind of Reinforcement Learning methods), named VNE-TD, is proposed. In VNE-TD, multiple embedding candidates of node-mapping are generated probabilistically, and TD Learning is involved to evaluate the long-run potential of each candidate. Extensive simulation results show that VNE-TD outperforms previous algorithms significantly in terms of both block ratio and revenue.

DYVINE: Fitness-Based Dynamic Virtual Network Embedding in Cloud Computing

Virtual network embedding (VNE) is the process of embedding the set of interconnected virtual machines onto the set of interconnected physical servers (PSs) in the cloud computing environment. The level of complexity of VNE problem increases when a large number of virtual machines with a set of resource demand need to be embedded onto a network of thousands of PSs. The key challenge of VNE is the efficient mapping of virtual networks (VNs), which may have dynamic resource demands. Existing solutions mainly emphasize on the embedding of static VN resulting in poor resource utilization and very low acceptance rate. To tackle such level of complexity in VNE, a fitness-based dynamic virtual network embedding (DYVINE) algorithm is proposed with the goal to maximize the resource utilization by maximizing the acceptance rate. Local and global fitness values of the virtual machines and VN, respectively, are used to utilize the maximum amount of physical resources. The proposed VNE algorithm allows the VN to be dynamic, which indicates that the structure and resource demand can be changed during its execution time. Furthermore, in order to reduce the embedding time in each time slot, a set of PSs is selected to host the VN instead of considering thousands of PSs, which may significantly increase the embedding time. The proposed embedding mechanism is evaluated through extensive simulation and is compared with similar existing embedding algorithms, which outperforms over others.

Cognitive Virtual Network Embedding Algorithm Based on Weighted Relative Entropy

Current Internet is designed by lots of service providers with different objects and policies which make the direct deployment of radically new architecture and protocols on Internet nearly impossible without reaching a consensus among almost all of them. Network virtualization is proposed to fend off this ossification of Internet architecture and add diversity to the future Internet. As an important part of network virtualization, virtual network embedding (VNE) problem has received more and more attention. In order to solve the problems of large embedding cost, low acceptance ratio (AR) and environmental adaptability in VNE algorithms, cognitive method is introduced to improve the adaptability to the changing environment and a cognitive virtual network embedding algorithmbased on weighted relative entropy (WRE-CVNE) is proposed in this paper. At first, the weighted relative entropy (WRE) method is proposed to select the suitable substrate nodes and paths in VNE. In WRE method, the ranking indicators and their weighting coefficients are selected to calculate the node importance and path importance. It is the basic of the WRE-CVNE. In virtual node embedding stage, the WRE method and breadth first search (BFS) algorithm are both used, and the node proximity is introduced into substrate node ranking to achieve the joint topology awareness. Finally, in virtual link embedding stage, the CPU resource balance degree, bandwidth resource balance degree and path hop counts are taken into account. The path importance is calculated based on the WRE method and the suitable substrate path is selected to reduce the resource fragmentation. Simulation results show that the proposed algorithm can significantly improve AR and the long-term average revenue to cost ratio (LTAR/CR) by adjusting the weighting coefficients in VNE stage according to the network environment. We also analyze the impact of weighting coefficient on the performance of the WRE-CVNE. In addition, the adaptability of the WRE-CVNE is researched in three different scenarios and the effectiveness and efficiency of the WRE-CVNE are demonstrated.

Security Aware Virtual Network Embedding Algorithm Using Information Entropy TOPSIS

Network virtualization is an effective manner to address the ossification issue of Internet architecture. Virtual network embedding is one of the most critical techniques in network virtualization environments. Several security problems about virtual network embedding are introduced due to the fact that virtual network embedding adds a virtual layer into the internet architecture. In this paper, we proposed an approach for security aware virtual network embedding called SA-VNE to address the security problems in virtual network embedding process. Firstly, we use the information entropy TOPSIS method to rank the importance of substrate nodes with an aim to choose the most appropriate substrate node for accommodating the virtual node. Secondly, we use the shortest path algorithm to perform the link mapping process. Simulation results demonstrated that our proposed SA-VNE algorithm behaves better that those of state-of-the-art existing security aware virtual network embedding algorithms in terms of the long-term average revenue, the long-term average VN acceptance ratio, the long-term average revenue to cost ratio and the running time.

A PSO based multi-domain virtual network embedding approach

This paper proposed a multi-domain virtual network embedding algorithm based on multi-controller SDN architecture. The local controller first selects candidate substrate nodes for each virtual node in the domain. Then the global controller abstracts substrate network topology based on the candidate nodes and boundary nodes of each domain, and applies Particle Swarm Optimization Algorithm on it to divide virtual network requests. Each local controller then embeds the virtual nodes of the divided single-domain virtual network requests in the domain, and cooperates with other local controllers to embed the inter-domain virtual links. Simulation experimental results show that the proposed algorithm has good performance in reducing embedding cost with good stability and scalability.

A Constrained Shortest Path Scheme for Virtual Network Service Management

Virtual network services that span multiple data centers are important to support emerging data-intensive applications in fields such as bioinformatics and retail analytics. Successful virtual network service composition and maintenance requires flexible and scalable “constrained shortest path management” both in the management plane for virtual network embedding (VNE) or network function virtualization service chaining (NFV-SC), as well as in the data plane for traffic engineering (TE). In this paper, we show analytically and empirically that leveraging constrained shortest paths within recent VNE, NFV-SC and TE algorithms can lead to network utilization gains (of up to 50%) and higher energy efficiency. The management of complex VNE, NFV-SC and TE algorithms can be, however, intractable for large scale substrate networks due to the NP-hardness of the constrained shortest path problem. To address such scalability challenges, we propose a novel, exact constrained shortest path algorithm viz., neighborhoods method (NM). Our NM uses novel search space reduction techniques and has a theoretical quadratic speed-up making it practically faster (by an order of magnitude) than recent branch-and-bound exhaustive search solutions. Finally, we detail our NM-based SDN controller implementation in a real-world testbed to further validate practical NM benefits for virtual network services.

A Self-Adaptive Virtual Network Embedding Algorithm Based on Software-Defined Networks

Network virtualization provides a promising tool to allow multiple virtual networks (VNs) to run on a shared substrate network (SN) simultaneously. VN embedding (VNE) is one of the key technologies of network virtualization. The main goal of VNE is to effectively map VN requests to the SN, which is efficiently utilizes the network resources. The emergence of software defined networks provides a platform for network virtualization to be used and promoted. In a real environment, the resource requirements of tenants are generally different. A single VN mapping algorithm can not effectively handle the multi-demand problem of tenants. We propose a self-adaptive VNE algorithm. VN requests are divided into different types by an adaptive algorithm, we use an integer linear programming formulation to solve VNE problem. This paper considers three different types of VN requests. Type 1 VN requests for high bandwidth requirements, type 2 VN requests for low latency requirements, and type 3 VN requests for high bandwidth requirements and latency requirements. The simulation results show that the virtual network embedding algorithm proposed in this paper can make full use of the SN resources and improve the overall revenue, while effectively dealing with the multi-demand problem of tenants.

Virtual Network Embedding Algorithm for Location-Based Identifier Allocation

Network virtualization allows multiple isolated virtual networks (VNs) to share the same substrate network (SN). VN embedding (VNE) algorithms can efficiently allocate the limited SN resources to VNs and assign a unique identifier to each VN. However, the fixed bit width of VN identifier in the packet header limits the number of VNs, and extending the bit width leads to the increase of the network traffic. In this paper, we consider the label-combination method to generate VN identifiers by combining the link-grained labels with location information. This method requires the efficient allocation of labels, but the existing VN embedding works only consider the CPU and bandwidth resources. To address this issue, we propose a novel embedding model that considers the label, CPU and bandwidth resource constraints. Furthermore, two window-based heuristic algorithms called VNE-LIA and VNE-iLIA using the greedy algorithm and the proximity principle are presented to solve the VNE problem. The simulation experiments show that our proposed algorithms increase the number of VN identifiers and the revenue to cost ratio under the different resource conditions of SN.

Topology-Oriented Virtual Network Embedding Approach for Data Centers

© 2018 IEEE. Currently, data centers have become an attractive candidate for users that require IT resources in the form of virtual networks to run their applications. Optimal mapping of the virtual network on the top of the substrate network with resource constraint is called virtual network embedding (VNE) problem. Most of the VNE algorithms are general algorithms for random topology and not suitable for data centers due to particular topological characteristics. To solve the VNE problem in data centers, this paper develops a topology-oriented algorithm based on the Discrete Particle Swarm Optimization (DPSO). We first develop a maximum spanning algorithm to compute the ranking of virtual nodes based on, not only its bandwidth and degree, but also its connectivity in the entire virtual network. Then, the virtual networks are embedded onto the substrate network according to the connectivity ranking result by a DPSO-based algorithm, in which we also propose a topological heuristic information of substrate network and combine it into the particle search process for boosting convergence speed and revenue/cost ratio of substrate network. The evaluation results show that the proposed algorithm can improve the optimization performance of VNE by comparing with a few existing algorithms.

Virtual Network Embedding Algorithm via Diffusion Wavelet

The great success of the Internet has promoted the development of digital industries and increased the demand for communication bandwidth. For example, ultrahigh-definition videos and vehicle networks require fast bandwidth speed and increase network connection density, respectively. High-bandwidth and high-density parallel communication drive the rapid development of network virtualization and 5G/6G technology. In a network virtualization environment, this new demand also brings new link resource allocation difficulties in existing substrate networks. To solve this far-reaching problem, this paper proposes a virtual network embedding algorithm via diffusion wavelet (VNE_DW), which is an unsupervised structure learning algorithm. Through the diffusion wavelet, the topology structure of nodes, connection density, and link volume among the nodes are comprehensively evaluated. Nodes that facilitate the link mapping success rate are preferentially selected. Experimental results demonstrate that the mapping success rate and revenue-cost ratio of VNE_DW outperform other state-of-the-art algorithms with high bandwidth and density.

Virtual Network Embedding in Fiber-Wireless Access Networks for Resource-Efficient IoT Service Provisioning

The rise of Industry 4.0 is pushing more machine devices into the connection of the communication network. Internet of Things (IoT) emerges as an enabling technique to support automatic information transmission and the universal interconnection among massive machine devices. Unlike the human type communication (HTC) in the conventional Internet, IoT features machine type communication (HTC) with small data granularity and massive connections, which impose new challenges on the network resource allocation earlier designed for HTC communication. One of the challenging issues is how we can manage the network resource for the IoT service provisioning. As a promising network architecture for the “last-mile” access in 5G communication, Fiber-Wireless (FiWi) access network compromises the high bandwidth capacity of the optical access network and the flexibility of wireless access network. Especially, network virtualization as an emerging technique provides FiWi with the feasibility for the coordinated bandwidth allocation of wireless access network and optical access network. The evolution of FiWi access network should be not only oriented to the technological breakthrough in regular traffic, but also be supportive of the emerging IoT traffic. In this paper, we focus on the resource allocation problem in FiWi access network supporting IoT service. We first analyze the dynamics of regular traffic and propose the traffic prediction method based on Q-Learning. Then, we propose a virtual network embedding algorithm to map exactly the virtual networks of IoT traffic to the idle resource of the virtual networks of regular traffic without degrading the performance of regular traffic. Thus, the proposed algorithm can improve the resource utilization more effectively. The simulation results demonstrate that the proposed algorithm can achieve a higher acceptance ratio and lower migration ratio of IoT traffic.

Node‐Fusion: Topology‐aware virtual network embedding algorithm for repeatable virtual network mapping over substrate nodes

SummaryCloud computing has become a new Internet application model, where network virtualization is recognized as an important technology for allowing multiple heterogeneous virtual networks (VNs) to coexist on a shared substrate network (SN). As demands in cloud computing increase, the scale of VN greatly increases as well, and providing an end‐to‐end SN to embed VNs in terms of scale is difficult. To utilize SN resources fully, we devise a topology‐aware Node‐Fusion algorithm, which is different from the traditional virtual network embedding (VNE) algorithms, for repeatable VNE over substrate nodes problem. We rank the resource of nodes through a novel solution by considering the CPU and bandwidth of adjacent link capacity and the number of adjacent links of each node as resources, and rank a node on the basis of resources. Furthermore, we embed several virtual nodes into the same substrate node together in accordance with Node‐Fusion interconnection value during the node mapping process, which can greatly improve the success ratio of the subsequent link mapping phase. Evaluation results confirm that Node‐Fusion outperforms traditional classical heuristics (Link‐opt, Node‐opt, and ORSTA), which are modified to fit into our model, with regard to acceptance ratio, long‐term revenue, long‐term cost, and revenue‐cost ratio.

Virtual Network Embedding Based on Topology Potential.

To improve the low acceptance ratio and revenue to cost ratio caused by the poor match between the virtual nodes and the physical nodes in the existing virtual network embedding (VNE) algorithms, we established a multi-objective optimization integer linear programming model for the VNE problem, and proposed a novel two-stage virtual network embedding algorithm based on topology potential (VNE-TP). In the node embedding stage, the field theory once used for data clustering was introduced and a node embedding function designed to find the optimal physical node. In the link embedding stage, both the available bandwidth and hops of the candidate paths were considered, and a path embedding function designed to find the optimal path. Extensive simulation results show that the proposed algorithm outperforms other existing algorithms in terms of acceptance ratio and revenue to cost ratio.

An algorithm for fragment-aware virtual network reconfiguration.

In view of the fact that the current online virtual network embedding algorithms do not consider the fragment resources generated in the embedding process deeply enough, resulting in the problem that the acceptance ratio and the revenue to cost ratio are both low, a mathematical model for virtual network reconfiguration is constructed and a heuristic algorithm for fragment-aware virtual network reconfiguration (FA-VNR) is proposed. The FA-VNR algorithm selects the set of virtual nodes to be migrated according to the fragment degrees of the physical nodes, and selects the best virtual node migration scheme according to the reduction of the fragment degrees of the physical nodes as well as the reduction of the embedding cost of the embedded virtual networks. Extensive simulation results show that the proposed FA-VNR algorithm not only can obviously improve the acceptance ratio and the revenue to cost ratio of the current online virtual network embedding algorithm, but also has better optimization effect than the existing virtual network reconfiguration algorithm.

Topology based reliable virtual network embedding from a QoE perspective

In the network virtualization environments, one of the most challenges is how to map the virtual networks (VNs) onto a shared substrate network managed by an infrastructure provider (InP), which is termed as virtual network embedding problem. Prior studies on this issue only emphasize on maximizing the revenue or minimizing the energy consumption while ignoring the reliability requirements of end-users. In our work, we incorporate the reliability probability into the virtual network embedding process with an aim to improve the QoS/QoE of end users from a new perspective. We devised two novel reliable virtual network embedding algorithms called RRW-MaxMatch and RDCC-VNE based on RW-MaxMatch and DCC-VNE, respectively. Extensive simulations demonstrated that the efficiency of our proposed algorithms is better than those of two primitive algorithms in terms of the reliability demands, the acceptance ratio of virtual networks and the long-term average revenue.

A coordinated virtual optical network embedding algorithm based on resources availability-aware over elastic optical networks

Abstract Recent innovations in network virtualization and elastic optical networks enable the flexible deployment of optical networks. However, the spectrum fragmentation and virtual optical links being embedded to the longer lightpaths decrease the spectrum efficiency of elastic optical networks. To address this issue, a coordinated virtual optical network embedding algorithm based on resources availability-aware (RA-CVONE) is proposed. Firstly, we design the node importance metric to embed the neighboring virtual nodes to the closer physical nodes, and then a coordinated embedding approach is adopted, which can further shorten the length of the lightpaths to which virtual optical links are embedded. Moreover, we jointly evaluate the resources availability of a physical lightpath and its node, then select the candidate lightpaths with less spectrum fragmentations and abundant resources for a virtual optical link. Based on the resources availability, a path selection strategy is proposed to determine a physical lightpath of which the vacant spectrum resources can be effectively used. Simulation results show that the RA-CVONE algorithm can significantly shorten the length of the physical lightpaths which are selected to embed virtual optical links, reduce the bandwidth blocking probability of virtual optical network requests, and improve the spectrum utilization of elastic optical networks.