Virtual Network Embedding Problem Research Articles

Virtual Network Embedding Through Graph Eigenspace Alignment

The purpose of this paper is to study tradeoffs between efficiency and precision for the general virtual network embedding (VNE) problem. To narrow the gap between solutions contributed by previous heuristic schemes and the optimal solution, and to ease the unacceptable computational burden of optimization approaches to VNE for large-scale networks, we propose the use of graph eigenspace notions for node mapping, that is, for associating virtual nodes to substrate nodes (SNs). We also contribute an inexact algorithm which projects all SNs to a 2-D eigenspace for generating a more efficient node mapping. There are some similarities between our method and numerous graph-matching strategies emerging in machine learning fields, but there are also some differences between the two, because the VNE problem is not strictly mathematical with respect to the mapping it seeks. Thus, we provide the relevant theoretical evidence to guarantee the solution quality of our scheme compared with a couple of previous VNE proposals. Simulation results demonstrate that our schemes can reach a better tradeoff point regarding runtime (that approaches the overhead of implementing eigendecomposition of a general matrix) and embedding quality metrics. Besides, our inexact algorithm using 2-D projection exhibits an improvement in quality over previous node-rank-based methods.

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.

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.

Parametrized complexity of virtual network embeddings

This paper makes the case for a parametrized complexity approach to tackle the fundamental but notoriously hard Virtual Network Embedding Problem. In particular, we show that the structure of the to-be-embedded virtual network requests can be exploited toward fast (i.e.,fixed-parameter tractable) approximation algorithms, using dynamic as well as linear programming algorithms. Our approach does provide formal guarantees on the runtime and solution quality and can safeguard also latency constraints. Using extensive computational experiments we demonstrate the practical relevance of our novel approach.

Energy efficient virtual network embedding for wireless multi-hop cellular networks using multi-commodity flow algorithm

With the rapid development of wireless mobile communication technologies, the trend of generalisation and centralisation requires variable communication devices and flexible network structures. To improve the transmission quality and extend the cell coverage, multi-hop relay network, as a wireless access network, has become an important part of communication system framework. Furthermore, to support seamless communication of multiple devices and services in heterogenous wireless networks with high resources utilisation, network virtualisation has been proposed to offer a flexible and scalable management. In this paper, we study the wireless multi-hop cellular network and the problem of virtual network embedding. We first analyse the wireless multi-hop cellular network scenario and establish a virtual network embedding model. After that we propose a minimum cost flow algorithm based on the multi-commodity flow algorithm. Thus the problem is transformed into a multi-commodity flow problem. We finally put forward an optimisation algorithm of Lagrange relaxation and sub-gradient algorithm to solve the problem. The simulation results show that the proposed multi-commodity flow algorithm can make full use of network resources, and improve the acceptance rate of virtual network requests, so as to improve the quality of service.

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.

A Divide-and-conquer Evolutionary Algorithm for Large-scale Virtual Network Embedding

The subgraph isomorphism problems, which aim to map subgraphs to a given graph, are widely seen in many applications and are usually nondeterministic polynomial-time complete (NP-complete). As a representative extension of the subgraph isomorphism problem, virtual network embedding (VNE) is a key problem in datacenter scheduling and network virtualization. Existing metaheuristic approaches to VNE problems tend to schedule networks as a whole. But when the problem scale grows, the performance of these approaches may degenerate due to the curse of dimensionality. In this article, we intend to propose a divide-and-conquer evolutionary algorithm with overlapping decomposition (ODEA) to solve large-scale VNE problems. First, realizing the fact that the decision variables in graph-based optimization problems like VNE are usually nonseparable, an overlapping decomposition method is introduced by investigating the characteristic of the network structure. In this method, the critical elements which have tight connections to many other nodes can belong to multiple subcomponents. As a result, the decision variables with tight connections can always be evolved together in multiple subcomponents. Second, to combine the subsolutions into a complete feasible solution, a competitive strategy is devised. Through the competition among critical elements, the optimizing information is shared among subcomponents, which can further improve the effectiveness of ODEA. The proposed ODEA can adopt different metaheuristics as the optimizer, and we conduct experiments on both the scenarios with a single virtual network and with a series of online networks. The experimental results verify that ODEA can significantly improve the performance of different metaheuristics in large-scale VNE problems.

Harnessing betweenness centrality for virtual network embedding in tree topologies

We examine the virtual network embedding problem with QoS constraints and formulate an approach that exploits the betweenness centrality of VNE requests to improve performance. A pay-per-use revenue model is introduced to evaluate the algorithm. An evaluation study using datacentre-like substrates and a wide area topology compares the approach with four embedding methods from the literature and reports on the average revenue rate, embedding success probability, average number of VNE deployments, cost, and impact of substrate failures on the operation of the VNEs, confirming the efficacy of the proposed approach.

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.

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.

How to Migrate From Operational LTE/LTE–A Networks to C–RAN With Minimal Investment?

By leveraging the fully–centralized and virtualized cloud radio access network (C–RAN) architecture over densely deployed small cells, mobile network operators (MNOs) are expected to meet the ever–increasing coverage and capacity demands. Towards this end, finding the optimal numbers, and locations of centralized unit (CU) pools, and centralizing the baseband units of eNBs at the optimal CU pools plays a pivotal role in curtailing the required investments in order to transit from legacy decentralized RAN (D–RAN) to C–RAN. In this paper, we propose an approach for MNOs to adopt the C–RAN architecture with minimal investment by using the available infrastructure (e.g., site locations and transmission links). Specifically, we propose a decentralized unit – CU (DU–CU) mapping algorithm, which effectively selects the quantity and the locations of CU pools and assigns the CU of each DU to the appropriate CU pool. We then compare the traffic aggregation gains of C–RAN and traditional D–RAN. Lastly, in order to quantify the total cost of ownership savings that can be obtained by employing the legacy network infrastructure while migrating to C–RAN, we compare this scenario with the C–RAN migration scenario in which there is no available infrastructure. In both scenarios, the mapping algorithms are formulated as virtual network embedding problems using integer linear programming techniques. The results of the simulations, conducted using data traffic of 26 eNBs (209 cells) of an operational LTE–A mobile network, reveal that significant saving can be obtained by employing the available mobile network infrastructure while migrating to C–RAN.

Online Virtual Network Embedding Based on Virtual Links’ Rate Requirements

Virtual network embedding (VNE) is the process of allocating substrate network resources to support virtualized networks (VNs), mapping each virtual node to one substrate node and each virtual link (VL) to one or more substrate links. VLs are mapped onto one or more substrate links in which, for the latter case, substrate links are connected through intermediate substrate nodes (ISNs). Current VNE approaches ignore the CPU capacity that ISNs require to forward traffic on the VLs mapped onto them while computing the VN embedding cost. We demonstrate that the CPU processing of ISNs required to forward VLs’ traffic is not negligible or constant and that, it is rather a function of the rate requirements of the VLs mapped onto them. We formulate the VLs’ forwarding rate requirements function (VLR2) to determine the CPU utilization that ISNs would require to forward the traffic at the rates required by the VLs. Based on the VLR2 function we formulate the VNE-VLR2 approach, which extends the VNE problem to a more realistic approach, where the VN embedding cost also considers the CPU needed to forward the VLs’ traffic at the required rates. Rather than proposing performance enhancements to current VNE approaches, this paper demonstrates that there are sensible differences in the performance of the VNE problem while considering the ISNs’ CPU utilization due to the VLs’ forwarding rate requirements. The differences of our VNE-VLR2 approach with respect to current VNE approaches in key metrics like VNE acceptance ratio, revenue-to-cost ratio and convergence time highlight the importance of this practical consideration for more accurate, realistic and quality of service-aware online VNE processes.

Stochastic virtual network embedding via accelerated Benders decomposition

The interest in network virtualization as a solution to the current ossification of the Internet has been recently revived. Virtual Network Embedding (VNE) is the central problem in network virtualization that determines mapping of Virtual Networks (VN) on the physical substrate network while satisfying VN requirements and the substrate network resource constraints. Much research has been conducted on the VNE problem with the assumption that the VN requirements are known beforehand. Nevertheless, the precise amounts of the requirements are uncertain in practice. The limited research that has considered the uncertainty focused on obtaining conservative solutions that increase embedding cost in terms of substrate network resource consumption. In this paper, we examine the VNE problem with the objective of minimizing embedding cost via obtaining a non-conservative solution in the case of the bandwidth requirements of virtual links are expressed as random variables with known distributions. For the first time, we formulate the problem using the stochastic programming framework and utilize the sample average approximation technique to solve it. To tackle its complexity, we decompose the problem based on Benders method which is accelerated by four proposed techniques. Extensive simulation results show that the proposed approach outperforms the conservative solution by about 40%–45%, and it is up to 7.5 times faster than the branch and bound method.

Online power aware coordinated virtual network embedding with 5G delay constraint

Solving virtual network embedding problem with delay constraint is a key challenge to realize network virtualization for current and future 5G core networks. It is an NP-Hard problem, composed of two sub-problems, one for virtual node embedding, and another one for virtual edges embedding, usually solved separately or with a certain level of coordination, which in general could result on rejecting some virtualization requests. Therefore, the main contributions of this paper focused on introducing an online power aware algorithm to solve the virtual network embedding problem using less resources and less power consumption, while considering end-to-end delay as a main embedding constraint. The new algorithm minimizes the overall power of the physical network through efficiently maximizing the utilization of the active infrastructure resources and putting into sleeping mode all non-active ones. Evaluations of the proposed algorithm conducted against the state of art algorithms, and simulation results showed that, when end-to-end delay was not included the proposed online algorithm managed to reduce the total power consumption of the physical network by 23% lower than the online energy aware with dynamic demands VNE algorithm, EAD-VNE. However, when end-to-end delay was included, it significantly influenced the whole embedding process and resulted on reducing the average acceptance ratios by 16% compared to the cases without delay.

Virtual Network Embedding Based on Computing, Network, and Storage Resource Constraints

Network virtualization can offer more flexibility and better maintainability for the current Internet through allowing multiple heterogeneous virtual networks (VNs) to share the network resource of a common infrastructure provider. The main challenge in this respect is the efficient embedding the virtual nodes and virtual links from the VN requests onto the limited substrate network resources. The notion of storage resource can exchange bandwidth resource to some extent gives us a hint that the efficient utilization of storage resource can relieve the bandwidth resource consumption. The existing VN embedding model does not consider the storage resource constraints on substrate nodes and virtual nodes, and does not keep up with the need of actual situation. In this paper, we propose a novel VN embedding model based on 3-D resource constraints including computing, network and storage, and devise two heuristic algorithms as the baseline algorithms to deal with the VN embedding problem. To our best of our knowledge, this is the first time to propose VN embedding problem based on 3-D resources including computing, network, and storage.

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.

Assignment of Virtual Networks to Substrate Network for Software Defined Networks

Assigning multiple virtual network resources to physical network resources, called virtual network embedding (VNE), is known to be non-deterministic polynomial-time hard (NP-hard) problem. Currently software-defined networking (SDN) is gaining popularity in enterprise networks to improve the customizability and flexibility in network management service and reduced operational cost. A central controller in SDNs is an important factor that we need to take care of when we want to assign virtual networks to physical resources. In this work, we address virtual network embedding problems for SDNs. Indeed, our objective is to propose a method to assign virtual networks in the substrate network with minimum physical resources, and also minimizing delays between the virtual network controller and the switches in the virtual network. Our proposed algorithm considers the link and node constraints such as CPU and bandwidth constraints which is necessary to consider when we try to solve virtual network embedding problems.

Noise-induced VNE method for software-defined infrastructure with uncertain delay behaviors

Software-defined infrastructure (SDI) provides virtualized infrastructures to customers by slicing computing resources and network resources. One of the important problems for deploying an SDI framework is to control the assignment of physical resources to a virtual network against changes of traffic demand and service demand. For this problem, the virtual network embedding (VNE) problem, which maps a virtual network to physical resources, has been addressed, but a centralized calculation was assumed. It is difficult to adopt the centralized approach as the size of infrastructure increases and the number of VN requests increases because the identification of current demand becomes more complicated. In this paper, we present a VNE method that works with limited information for large, complicated, and uncertain SDI frameworks. To achieve this, our method applies the biological “Yuragi” principle. Yuragi is a Japanese word whose English translation is “a small perturbation to the system.” Yuragi is a mechanism that provides adaptability of organisms and is often expressed as an attractor selection model. This paper develops a Yuragi-based VNE method that deals with node attributes, has the generality to handle a performance objective, and runs in multi-slice environments. Simulation results show that the Yuragi-based method decreases VN migrations by about 29% relative to a heuristic method to adapt to fluctuations in resource requirements.