Virtual Network Embedding Problem Research Articles

A Constructive Particle Swarm Optimizer for Virtual Network Embedding

Virtual network embedding (VNE) is a key technique for flexible sharing of physical resources in the modern Internet. As the VNE problem is nondeterministic polynomial hard, natural inspired population-based algorithms have been increasingly considered as promising approaches. But as the VNE problem has two tightly related tasks: node mapping and link mapping, it remains challenging for metaheuristics to deal with both tasks coordinately and effectively. This paper proposes a constructive particle swarm optimizer for virtual network embedding (CPSO-VNE). CPSO-VNE uses discrete vectors and matrices with probabilities to encode positions and velocities on VNE search space, respectively. What makes CPSO-VNE different from previous works is the step-by-step solution construction scheme introduced in CPSO-VNE. In this scheme, each node is mapped along with the mapping of its adjacent virtual links. In this way, node and link mappings are coordinated in one stage. In addition, with this step-by-step construction scheme, path information of networks can be introduced as heuristic information to guide the search, which can further improve performance. The proposed method is tested on the scenarios with a single VN request and with a set of online VN requests. The simulation results show that the proposed approach is promising.

A single-player Monte Carlo tree search method combined with node importance for virtual network embedding

As a critical technology in network virtualization, virtual network embedding (VNE) focuses on how to allocate physical resources to virtual network requests efficiently. Because the VNE problem is NP-hard, most of the existing approaches are heuristic-based algorithms that tend to converge to a local optimal solution and have a low performance. In this paper, we propose an algorithm that combines the basic Monte Carlo tree search (MCTS) method with node importance to apply domain-specific knowledge. For a virtual network request, we first model the embedding process as a finite Markov decision process (MDP), where each virtual node is embedded in one state in the order of node importance that we design. The shortest-path algorithm is then applied to embed links in the terminal state and return the cost as a part of the reward. Due to the reward delay mechanism of the MDP, the result of link mapping can affect the action selected in the previous node mapping stage, coordinating the two embedding stages. With node importance, domain-specific knowledge can be used in the Expansion and Simulation stages of MCTS to speed up the search and estimate the simulation value more accurately. The experimental results show that, compared with the existing classic algorithms, our proposed algorithm can improve the performance of VNE in terms of the average physical node utilization ratio, acceptance ratio, and long-term revenue to cost ratio.

On Virtual Network Embedding: Paths and Cycles

Network virtualization provides a promising solution to overcome the ossification of current networks, allowing multiple Virtual Network Requests (VNRs) to be embedded on a common infrastructure. The major challenge in network virtualization is the Virtual Network Embedding (VNE) problem, which is to embed VNRs onto a shared substrate network and known to be $\mathcal {NP}$ -hard. The topological heterogeneity of VNRs is one important factor hampering the performance of the VNE. However, in many specialized applications and infrastructures, VNRs are of some common structural features, e.g., paths and cycles. To achieve better outcomes, it is thus critical to design dedicated algorithms for these applications and infrastructures by taking into accounting topological characteristics. Besides, paths and cycles are two of the most fundamental topologies that all network structures consist of. Exploiting the characteristics of path and cycle embeddings is vital to tackle the general VNE problem. In this paper, we investigate the path and cycle embedding problems. For path embedding, we prove its $\mathcal {NP}$ -hardness and inapproximability. Then, by utilizing Multiple Knapsack Problem (MKP) and Multi-Dimensional Knapsack Problem (MDKP), we propose an efficient and effective MKP-MDKP-based algorithm. For cycle embedding, we propose a Weighted Directed Auxiliary Graph (WDAG) to develop a polynomial-time algorithm to determine the least-resource-consuming embedding. Numerical results show our customized algorithms can boost the acceptance ratio and revenue compared to generic embedding algorithms in the literature.

A Formal Approach to Verify Connectivity and Optimize VNF Placement in Industrial Networks

The increased flexibility and interconnectivity of modern industrial communication networks, obtained through the use of innovative technologies like network function virtualization and software-defined networking, require a secure and manageable framework to support the new communication and computing needs. To focus on these requirements, this article proposes a framework for reliable placement of services across physically separated locations, which offers both system optimization, in terms of latency and resource utilization, and connectivity policy enforcement to guarantee service reliability, safety, and security. This is achieved by exploiting a new approach to solve the virtual network embedding problem, using optimization modulo theories (MaxSMT), which allows the use of very expressive constraints.

Security-Aware Virtual Network Embedding Algorithm Based on Reinforcement Learning

Virtual network embedding (VNE) algorithm is always the key problem in network virtualization (NV) technology. At present, the research in this field still has the following problems. The traditional way to solve VNE problem is to use heuristic algorithm. However, this method relies on manual embedding rules, which does not accord with the actual situation of VNE. In addition, as the use of intelligent learning algorithm to solve the problem of VNE has become a trend, this method is gradually outdated. At the same time, there are some security problems in VNE. However, there is no intelligent algorithm to solve the security problem of VNE. For this reason, this paper proposes a security-aware VNE algorithm based on reinforcement learning (RL). In the training phase, we use a policy network as a learning agent and take the extracted attributes of the substrate nodes to form a feature matrix as input. The learning agent is trained in this environment to get the mapping probability of each substrate node. In the test phase, we map nodes according to the mapping probability and use the breadth-first strategy (BFS) to map links. For the security problem, we add security requirements level constraint for each virtual node and security level constraint for each substrate node. Virtual nodes can only be embedded on substrate nodes that are not lower than the level of security requirements. Experimental results show that the proposed algorithm is superior to other typical algorithms in terms of long-term average return, long-term revenue consumption ratio and virtual network request (VNR) acceptance rate.

Virtual Network Embedding for Multi-Domain Heterogeneous Converged Optical Networks: Issues and Challenges.

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.

VNE strategy based on chaos hybrid flower pollination algorithm considering multi-criteria decision making

With the development of science and technology and the need for multi-criteria decision making (MCDM), the optimization problem to be solved becomes extremely complex. The theoretically accurate and optimal solutions are often difficult to obtain. Therefore, meta-heuristic algorithms based on multi-point search have received extensive attention. The flower pollination algorithm (FPA) is a new swarm intelligence meta-heuristic algorithm, which can effectively control the balance between global search and local search through a handover probability, and gradually attracts the attention of researchers. However, the algorithm still has problems that are common to optimization algorithms. For example, the global search operation guided by the optimal solution is easy to lead the algorithm into local optimum, and the vector-guided search process is not suitable for solving some problems in discrete space. Moreover, the algorithm does not consider dynamic multi-criteria decision problems well. Aiming at these problems, the design strategy of hybrid flower pollination algorithm for virtual network embedding problem is discussed. Combining the advantages of the genetic algorithm and FPA, the algorithm is optimized for the characteristics of discrete optimization problems. The cross-operation is used to replace the cross-pollination operation to complete the global search and replace the mutation operation with self-pollination operation to enhance the ability of local search. Moreover, a life cycle mechanism is introduced as a complement to the traditional fitness-based selection strategy to avoid premature convergence. A chaos optimization strategy is introduced to replace the random sequence-guided crossover process to strengthen the global search capability and reduce the probability of producing invalid individuals. In addition, a two-layer BP neural network is introduced to replace the traditional objective function to strengthen the dynamic MCDM ability. Simulation results show that the proposed method has good performance in link load balancing, revenue???cost ratio, VN requests acceptance ratio, mapping average quotation, average time delay, average packet loss rate, and the average running time of the algorithm.

Failure Aware Semi-Centralized Virtual Network Embedding in Cloud Computing Fat-Tree Data Center Networks

In Cloud Computing, the tenants opting for the Infrastructure as a Service (IaaS) send the resource requirements to the Cloud Service Provider (CSP) in the form of Virtual Network (VN) consisting of a set of inter-connected Virtual Machines (VM). Embedding the VN onto the existing physical network is known as Virtual Network Embedding (VNE) problem. One of the major research challenges is to allocate the physical resources such that the failure of the physical resources would bring less impact onto the users' service. Additionally, the major challenge is to handle the embedding process of growing number of incoming users' VNs from the algorithm design point-of-view. Considering both of the above-mentioned research issues, a novel Failure aware Semi-Centralized VNE (FSC-VNE) algorithm is proposed for the Fat-Tree data center network with the goal to reduce the impact of the resource failure onto the existing users. The impact of failure of the Physical Machines (PMs), physical links and network devices are taken into account while allocating the resources to the users. The beauty of the proposed algorithm is that the VMs are assigned to different PMs in a semi-centralized manner. In other words, the embedding algorithm is executed by multiple physical servers in order to concurrently embed the VMs of a VN and reduces the embedding time. Extensive simulation results show that the proposed algorithm can outperform over other VNE algorithms.

On the Hardness and Inapproximability of Virtual Network Embeddings

Many resource allocation problems in the cloud can be described as a basic Virtual Network Embedding Problem (VNEP): the problem of finding a mapping of a request graph (describing a workload) onto a substrate graph (describing the physical infrastructure). Applications range from mapping testbeds, over the embedding of batch-processing tasks to the embedding of service function chains and come with different mapping restrictions for nodes and edges. The restrictions studied most often are node and edge capacities, node mapping, edge routing and latency restrictions. While the VNEP has been studied intensively, complexity results are only known for specific models and this paper provides a first comprehensive study of the computational complexity of the VNEP by systematically analyzing its hardness for any combination of the above stated mapping restrictions. For all studied variants the ${ {\mathcal {NP}}\text {-completeness}}$ of the respective decision problems is shown. Furthermore, ${ {\mathcal {NP}}\text {-completeness}}$ results for finding approximate embeddings, which may, e.g., violate capacity constraints by certain factors, are derived. Lastly, it is also shown that all these results pertain when restricting the request graphs to planar and degree-bounded graphs. While theoretic in nature, our results have severe practical implications. Firstly, any optimization variant of the VNEP is ${ {\mathcal {NP}}\text {-hard}}$ and cannot be approximated for any of the studied restrictions, unless ${ {\mathcal {P}}{\,=\,} {\mathcal {NP}}}$ . Secondly, we uncover structural hardness properties: the VNEP is ${ {\mathcal {NP}}\text {-hard}}$ and inapproximable even if, e.g., only node placement and edge routing restrictions are considered.

Survivable IaaS Management with SDN

Fault-tolerance, survivability and resiliency in wide area networks have long been prominent research topics. With the popularity of the cloud service model and the novel software defined networking (SDN) paradigm, there is renewed interest in failure protection and restoration in network service provisioning. In this work, we propose a novel protection and restoration based virtual network management scheme to enhance fault tolerance in infrastructure-as-a-service, deployed over networked cloud infrastructure. The networked cloud infrastructure is composed of multiple geographically distributed datacenters that are interconnected with SDN. Both compute and network resources are allocated by formulating the virtual network embedding problem as an integer linear program. A shared backup virtual link protection mechanism and a reactive traffic engineering network failure restoration algorithm are proposed and integrated with the framework to provide recovery from unexpected link failures. We implemented the framework on an emulated SDN testbed and evaluated the performances of the algorithms during single and multiple link failures. Experimental results demonstrate trade-offs of the proposed approaches and their applicability in different application scenarios.

Congestion-Aware and Energy-Aware Virtual Network Embedding

Network virtualization is an inherent component of future internet architectures. Network resources are virtualized from the underlying substrate and elastically provisioned and offered to customers on-demand. Optimal allocation of network resources in terms of utilization, quality of service, and energy consumption has been a challenge. Existing solutions consider congestion control in a single-objective virtual network embedding (VNE) problem. This paper defines a multiple-objective VNE problem called the congestion-aware, energy-aware VNE (CEVNE). The aim is to seek a solution that saves cost, saves energy and avoids network congestion simultaneously. CEVNE modelling techniques and solution approaches apply both the weighting method and the constraint method to search for pareto-optimal solutions that produce the best compromised solutions for all three objectives. Solving VNE problem is, however, NP-hard. A heuristic solution is proposed involving a two-stage coordinated CEVNE. The node-mapping algorithm searches for the sub-optimal solutions for three objectives. The link mapping process is an SDN-based heuristic algorithm that deploys a path service and a resource monitoring application on an SDN controller. The solution is realized using SDN, Segment Routing, and open network operating system platform (ONOS) technologies. The energy minimization is implemented with a registry that keeps track of active nodes and sets inactive nodes to sleep mode. The evaluation results showed that the multiple-objective CEVNE approach is feasible and achieves its goals of optimizing the resource allocation, improving the runtime, saving the energy consumption and controlling the network congestion.

Early-Warning-Time-Based Virtual Network Live Evacuation Against Disaster Threats

Virtualization facilitates heterogeneous cloud applications to share the same physical infrastructure with admirable flexibility, while resource efficiency and survivability are critical concerns for virtual network embedding (VNE). As more and more internet applications migrate to the cloud, the resource efficiency and the survivability of VNs, such as single link failure or large-scale disaster survivability, have become crucial issues. Separating the VNE problem into node and link mapping sub-problems without coordination might cause a high embedding cost. This dissertation presents two independent approaches to solve the aforementioned challenges. First, we study two-stage coordinated survivable VNE (SVNE) problem and propose an adaptive path splitting based SVNE (APSS) scheme. We first develop a concise anchor node strategy to restrict the solution space of the candidate substrate nodes, which coordinates node mapping with link mapping to limit the distance spans of the virtual links. Then, we employ an adaptive path splitting policy to provide full protection against single-link failures with partial backup resource, and design an agile frequency slot windows choosing mechanism to mitigate the spectrum fragmentation for link resource efficiency. Simulation results demonstrate that the proposed APSS scheme can achieve satisfactory performance in terms of spectrum utilization and blocking ratio. Second, we propose a synchronous evacuation strategy for VNs with dual virtual machines (VMs) inside a disaster risk zone (DRZ), which suffer higher risks than the VNs with single. The evacuation strategy exploits post-copy technique to sustain the online service alive and enhances synchronous VM migrations to shorten the dual-VM evacuation time. Numerical results show that the proposed strategy can outperform the best-effort scheme in terms of average and total evacuation times of dual-VMs.

Energy Optimized Virtual Network Embedding With Location Constraint in the Enterprise Network

To satisfy enterprise demands of analyzing and dealing with the large scale of data with lower costs, an effective method is to integrate the servers and computers and use virtualization technology to construct an enterprise network. Prior studies on network virtualization have mainly been executed in the cloud; however, these studies may not be appropriate for enterprise networks for two reasons: i) the goal of most of them is to generate more revenues for cloud providers, but focus less on saving costs; ii) the physical machines are relatively concentrated in the cloud platform but dispersed over different geographic locations in enterprise networks. In this paper, we solve the problem of energy-optimized virtual network embedding with location constraints (EO-VNE). First, the node and link capabilities in enterprise networks are defined in the form of complex number theory, unifying computers and virtual requests. Second, the normalized method of computing and storage capabilities are proposed to identify the node capability. Third, an energy model of the enterprise network is built, and using this model, EO-VNE is shown to be NP-complete. Finally, an energy-optimized virtual network embedding with a location constraint algorithm (EOLC) is proposed to minimize the energy consumption under the constraint of node position. The experiments show that EOLC consumes less energy compared with the algorithm of energy-aware virtual network embedding with dynamic demands (EAD). It also has better performance than the location constraint algorithm based on bisection (GLC).

VNE-SDN Algorithms for Different Physical Network Environments

Network virtualization (NV) is widely considered as one of the key technologies for future networks, which allows multiple virtual networks (VNs) to run on the same substrate network simultaneously. Software defined networking (SDN) can be used as a platform to realize NV. Therefore, the NV-based SDN has attracted intensive attention from both academic and industry in recent years. In this article, we focus on virtual network embedding (VNE) problem, which is one of the most important technologies of NV. Specifically, resource situation of underlying network (RSUN) is not considered in previous VNE algorithms, which seriously affects the efficiency of VNE. To solve this problem, we develop two heuristic algorithms to select the appropriate VNE according to RSUN. Our proposed algorithms adopt a novel node-ranking method, which fully considers the node topology and resource attributes, to sort physical nodes. In addition, two sorting approaches for virtual nodes and the global matching embedding (GME) approach are proposed. Our numerical analyses validate our proposed schemes and algorithms in terms of the average revenue to cost ratio, the average revenue, and the average VN request acceptance ratio.

Virtual Network Embedding With Dynamic Speed Switching Orchestration in Fog/Edge Network

In the future 5G networks, network deployment flexibility and low network latency are two of the most critical requirements and issues. Recently, network virtualization and Fog/Edge computing have been proposed as two potential solutions to enable the desired future network environment. This paper investigates the Virtual Network Embedding (VNE) problem in a Multi-access Edge Computing (MEC) architecture, according to the standards proposed by European Telecommunications Standards Institute (ETSI). We propose an embedding algorithm, called PSO-CSNR, to optimize end-to-end latency constraints in an MEC network. In addition, we adopt Activity on Vertex (AOV) network as our Virtual Network Request (VNR), which is more realistic to real applications. Moreover, we consider the latest processor technologies for substrate nodes, where the CPUs are deployed with asymmetric core frequencies, and propose the second algorithm, called DSS. The DSS can dynamically orchestrate the processing speed of each virtual function, in order to decrease the processing time of virtual functions on virtual nodes, so that the Infrastructure Providers (InPs) can gain more profit in the same amount of time. We then combine the PSO-CSNR with DSS, and refer to it as VNE-DSSO. The simulation results show that the VNE-DSSO algorithm outperforms the other existing algorithms in terms of revenue, acceptance ratio and embedding cost.

A survey of embedding algorithm for virtual network embedding

Network virtualization (NV) is pushed forward by its proponents as a crucial attribute of next generation network, aiming at overcoming the gradual ossification of current networks, particularly to the worldwide Internet. Through virtualization, multiple customized virtual networks (VNs), requested by users, are allowed to coexist on the underlying substrate networks (SNs). In addition, the virtualization scheme contributes to sharing underlying physical resources simultaneously and seamlessly. However, multiple technical issues still stand in the way of NV successful implementation. One key technical issue is virtual network embedding (VNE), known as the resource allocation problem for NV. This paper conducts a survey of embedding algorithms for VNE problem. At first, the NV business model for VNE problem is presented. Then, the latest VNE problem description is presented. Main performance metrics for evaluating embedding algorithms are also involved. Afterwards, existing VNE algorithms are detailed, according to the novel proposed category approach. Next, key future research aspects of embedding algorithms are listed out. Finally, the paper is briefly concluded.

Adaptive path splitting based survivable virtual network embedding in elastic optical networks

Abstract Virtualization facilitates heterogeneous cloud applications to share the same physical infrastructure with admirable flexibility, while resource efficiency and survivability are critical concerns for virtual network embedding (VNE). In this paper, we study two-stage coordinated survivable VNE (SVNE) problem and propose an adaptive path splitting based SVNE (APSS) scheme for elastic optical networks (EONs). For APSS, we first develop a concise anchor node strategy to balance the node resource utilization and restrict the solution space of the candidate substrate nodes, which coordinates node mapping with link mapping to limit the distance spans of the virtual links. Then, we employ an adaptive path splitting policy to provide full protection against single-link failures with partial backup resource, and design an agile frequency slot windows choosing mechanism to mitigate the spectrum fragmentation for link resource efficiency. We evaluate the proposed APSS scheme and compare its performances with some counterpart schemes. Simulation results demonstrate that the proposed APSS scheme can achieve satisfactory performance in terms of spectrum utilization and blocking ratio, even if with higher backup redundancy ratio.

EPVNE: An Efficient Parallelizable Virtual Network Embedding Algorithm

Virtual network embedding (VNE) problem is a key issue in network virtualization technology, and much attention has been paid to the virtual network embedding. However, very little research work focuses on parallelized virtual network embedding problems which assumes that the substrate infrastructure supports parallel computing and allows one virtual node to be mapped to multiple substrate nodes. Based on the work of Liang and Zhang, we extend the well-known VNE to parallelizable virtual network embedding (PVNE) in this paper. Furthermore, to the best of our knowledge, we give the first formulation of the PVNE problem. A new heuristic algorithm named efficient parallelizable virtual network embedding (EPVNE) is proposed to reduce the cost of embedding the VN request and increase the VN request acceptance ratio. EPVNE is a two-stage mapping algorithm, which first performs node mapping and then performs link mapping. In the node mapping phase, we present a simple and efficient virtual node and physical node sorting formula and perform the virtual node mapping in order. When mapping virtual nodes, we map virtual nodes to physical nodes that just meet the CPU requirements. Substrate nodes with more CPU resources will be retained for subsequent virtual network mapping requests. In the link mapping phase, Dijkstra’s algorithm is used to find a substrate path for each virtual link. Finally, simulations are carried out and simulation results show that our algorithm performs better than the existing heuristic algorithms.

Multi-Objective Optimization-Based Virtual Network Embedding Algorithm for Software-Defined Networking

To overcome the drawbacks of traditional Internet architectures, software-defined networking (SDN) technology has been proposed, which is expected to dramatically simplify network control processes and enable the convenient deployment of sophisticated network functions. To achieve highly efficient resource utilization in SDN and offer users with diverse service requirements, virtual network embedding (VNE), which maps various virtual network requests of users to a given substrate network, should be conducted. In this paper, we study the VNE problem in SDN where the substrate SDN switches and links may be subject to malicious attacks. We first propose a hierarchical virtualization-enabled SDN architecture based on which the VNE strategy can be designed. Then, stressing the importance of network load and reliability of the substrate network, we formulate the VNE problem of SDN as a multi-objective optimization problem which jointly minimizes network load and maximizes embedding reliability under the constraints of virtual network requirements and the resource characteristics of substrate network. As the formulated optimization problem is a complicated multi-objective optimization problem which cannot be solved conveniently, we apply the ideal point method. In particular, we first propose virtual node embedding sub-algorithm and virtual link embedding sub-algorithm to determine the locally optimal solution to the two subproblems, i.e., network load minimization subproblem and embedding reliability maximization subproblem. Then, examining the distance between the feasible solutions and the locally optimal solutions, we formulate a single-objective optimization problem and solve the problem to obtain the global VNE strategy by applying discrete particle swarm optimization (DPSO) algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm.

Virtual Network Embedding Approximations: Leveraging Randomized Rounding

The Virtual Network Embedding Problem (VNEP) captures the essence of many resource allocation problems. In the VNEP, customers request resources in the form of Virtual Networks. An embedding of a virtual network on a shared physical infrastructure is the joint mapping of (virtual) nodes to physical servers together with the mapping of (virtual) edges onto paths in the physical network connecting the respective servers. This work initiates the study of approximation algorithms for the VNEP for general request graphs. Concretely, we study the offline setting with admission control: given multiple requests, the task is to embed the most profitable subset while not exceeding resource capacities. Our approximation is based on the randomized rounding of Linear Programming (LP) solutions. Interestingly, we uncover that the standard LP formulation for the VNEP exhibits an inherent structural deficit when considering general virtual network topologies: its solutions cannot be decomposed into valid embeddings. In turn, focusing on the class of cactus request graphs, we devise a novel LP formulation, whose solutions can be decomposed. Proving performance guarantees of our rounding scheme, we obtain the first approximation algorithm for the VNEP in the resource augmentation model. We propose different types of rounding heuristics and evaluate their performance in an extensive computational study. Our results indicate that good solutions can be achieved even without resource augmentations. Specifically, heuristical rounding achieves 77.2% of the baseline’s profit on average while respecting capacities.