Virtual Network Embedding Research Articles

Path Mapping Approach for Network Function Virtualization Resource Allocation with Network Function Decomposition Support

Recently, Network Function Virtualization (NFV) and Software Defined Networking (SDN) have attracted many mobile operators. For the flexible deployment of Network Functions (NFs) in an NFV environment, NF decompositions and control/user plane separation have been introduced in the literature. That is to map traditional functions into their corresponding Virtual Network Functions (VNFs). This mapping requires the NFV Resource Allocation (NFV-RA) for multi-path service graphs with a high number of virtual nodes and links, which is a complex NP-hard problem that inherited its complexity from the Virtual Network Embedding (VNE). This paper proposes a new path mapping approach to solving the NFV-RA problem for decomposed Network Service Chains (NSCs). The proposed solution has symmetrically considered optimizing an average embedding cost with an enhancement on average execution time. The proposed approach has been compared to two other existing schemes using 6 and 16 scenarios of short and long simulation runs, respectively. The impact of the number of nodes, links and paths of the service requests on the proposed scheme has been studied by solving more than 122,000 service requests. The proposed Integer Linear Programming (ILP) and heuristic schemes have reduced the execution time up to 39.58% and 6.42% compared to existing ILP and heuristic schemes, respectively. Moreover, the proposed schemes have also reduced the average embedding cost and increased the profit for the service providers.

Dynamic Embedding and Quality of Service-Driven Adjustment for Cloud Networks

Cloud computing built on virtualization technologies can provide Internet service providers (SPs) with elastic virtualized node and link resources. SPs can outsource their virtualized resources as customized virtual networks (VNs) to end users. Hence, how to efficiently embed these VNs is the core issue in virtualization research. This technical issue is virtual network embedding (VNE). Since the issue inception, multiple mapping algorithms have been studied, including the reinforcement learning (RL) approach of machine learning. However, prior mapping algorithms are mostly static. Existing dynamic mapping algorithms just focus on accepting as many VNs as possible. No existing dynamic algorithm considers optimizing the quality of service (QoS) performance of each accepted VN. Optimizing the VN QoS performance is beneficial to guaranteeing service quality in cloud computing environment. On these backgrounds, we jointly investigate the dynamic VN embedding and optimize the QoS performance of each accepted VN. A dynamic heuristic algorithm is proposed in order to be evaluated in continuous time. When one VN service is requested, the VN will be mapped by the dynamic heuristic algorithm. If the QoS demand of the VN is not guaranteed, the reembedding scheme of the heuristic algorithm will be driven. Certain virtual elements of the VN will be adjusted. The dynamic embedding algorithm ensures flexible VN assignment and fulfills customized QoS demands. Finally, simulation results are illustrated in order to validate the strength of our dynamic algorithm. We perform the comparison with multiple existing dynamic algorithms. For instance, VN acceptance ratio of our dynamic heuristic algorithm improves at least 13 $\%$ .

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.

Self‐adaptive online virtual network migration in network virtualization environments

AbstractIn Network Virtualization Environments, the capability of operators to allocate resources in the Substrate Network (SN) to support Virtual Networks (VNs) in an optimal manner is known as Virtual Network Embedding (VNE). In the same context, online VN migration is the process meant to reallocate components of a VN, or even an entire VN among elements of the SN in real time and seamlessly to end‐users. Online VNE without VN migration may lead to either over‐ or under‐utilization of the SN resources. However, VN migration is challenging due to its computational cost and the service disruption inherent to VN components reallocation. Online VN migration can reduce migration costs insofar it is triggered proactively, not reactively, at critical times, avoiding the negative effects of both under‐ and over‐triggering. This paper presents a novel online cost‐efficient mechanism that self‐adaptively learns the exact moments when triggering VN migration is likely to be profitable in the long term. We propose a novel self‐adaptive mechanism based on Reinforcement Learning that determines the right trigger online VN migration times, leading to the minimization of migration costs while simultaneously considering the online VNE acceptance ratio.

Secure Multi-Cloud Network Virtualization

Existing network virtualization systems share a few characteristics, namely they target one data center of a single operator and only offer traditional networking services. As such, their support for critical applications that need to be deployed across multiple trust domains, while enforcing diverse security requirements, is limited. This paper enhances the state-of-the-art by presenting a multi-cloud network virtualization system, allowing the provision of virtual networks of containers. Our solution enables a provider to enrich its network substrate with public and private cloud-based resources, increasing flexibility and the range of supplied services. One challenging aspect that we tackle is the embedding of virtual network requests to the substrate infrastructure, as existing work is unfit to a modern data center context, scales poorly or does not consider the security of virtual resources. We propose a scalable heuristic that considers security as a first-class citizen and is specifically tailored to a hybrid multi-cloud domain. We evaluate our algorithm with large-scale simulations that consider realistic network topologies and our prototype in a substrate composed of one private data center and two public clouds. The system scales well for networks of thousands of switches employing diverse topologies and improves on the virtual network acceptance ratio, provider revenue, and embedding delays. Our results show that the acceptance ratios are less than 1% from the optimal and that the system can provision a 10 thousand container virtual network in approximately 2 minutes.

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.

Topological Consistency-Based Virtual Network Embedding in Elastic Optical Networks

Topological Consistency-Based Virtual Network Embedding in Elastic Optical Networks

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.

EE-CTA: Energy efficient, concurrent and topology-aware virtual network embedding as a multi-objective optimization problem

Network virtualization provides the substruction of various heterogeneous Virtual Networks (VNs) on a single physical infrastructure which is called Virtual Network Embedding (VNE) and is known as an Np-hard problem. The VNE includes two sub-problem; virtual node mapping and virtual link mapping. Related works do not consider network topology and energy efficiency in the embedding process. This paper proposes Energy Efficient, Concurrent, and Topology-Aware (EE-CTA) algorithm as a new concurrent VNE method. Also, EE-CTA is energy-efficient due to using servers status and renewable energy resources when they are available. Our proposed EE-CTA has focused on network topology with assigning reachability rank to resources. In order to achieve all of these goals, we model VNE as a multi-objective optimization problem and solve it by Non-dominated Sorting Genetic Algorithm (NSGA-II). We compare EE-CTA with Presto, Ant Colony Optimization (ACO), Topology and Migration-Aware Energy Efficient (TMAE), and RW-Max match methods. The evaluation results demonstrate our method improves revenue, acceptance ratio, cost, and energy usage.

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.

Green virtual network embedding with supervised self-organizing map

Virtualization in the data center network is used to overcome the resistance of Internet ossification. Node mapping in virtual network embedding (VNE) process is more important than link mapping because each node mapping has a lot of choices with high probability. Historical data always have valuable information which displays how to be mapped virtual networks with special features. Previous works use physical network historical information, and they do not pay attention to the characteristics of virtual networks during the embedding process. To address this problem, this paper proposes SoGVNE (Self-organized Green Virtual Network Embedding) which uses previous successful virtual node mapping information to train a self-organizing map (SOM) neural network. Then, it will respond to future requests quickly and with fewer resources. SoGVNE uses virtual networks historical data and is able to embed more than one virtual network concurrently. Using supervised SOM makes the SoGVNE aware of resources with renewable energy and uses them to manage energy consumption. This paper evaluates SoGVNE with reinforcement-learning, Monte-Carlo and Presto methods. The results indicate the high efficiency of the proposed method in learning the node mapping process and achieving high profit and acceptance ratio with low cost.

Resource Allocation Over EON-Based Infrastructures in a Network Virtualization Environment

Through network virtualization, new networking paradigms can be easily implemented by sharing a common physical infrastructure between different users and applications. A fundamental aspect in this environment is how to efficiently map virtual resources onto physical resources, frequently referred as virtual network embedding. Integer linear programming, heuristics and meta-heuristics have been commonly used to solve this problem. However, efficient allocation algorithms cannot ignore the main characteristics of the physical infrastructure. Simultaneously, elastic optical networks (EONs), are gaining attention of researchers and industry due to their high spectrum efficiency, flexibility and adaptability that allow higher transmission rates able to cope with the tremendous IP traffic demand of new applications and services. In this paper, we formulate a mathematical model for the offline virtual optical network embedding assuming a transparent EON-based infrastructure. We then propose a meta-heuristic (PRVONE) based on an optical-path ranking system to provide scalable embeddings within reasonable running time. We evaluate our work through extensive simulations and compare it with state-of-the-art approaches. Results show that PRVONE can achieve improvements in terms of acceptance ratio in all test case scenarios while providing load balanced embeddings.

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.

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.

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 federated software‐defined networks

SummaryIn this paper, we focus on energy efficient virtual network embedding in federated (multidomain) software‐defined networks (SDNs). We first formulate an optimization problem as an integer linear program (ILP) that minimizes the energy consumption of the network links, while at the same time adhering to the bandwidth and CPU requirements of the virtual network requests. We then propose a polynomial‐time heuristic algorithm, which consists of three stages. In the first stage, the top SDN controller decides on whether to partition the virtual network request into smaller subrequests and give subrequests to multiple domains or give the enitre virtual network request to a single domain, while in the second stage, each SDN controller implements virtual network embedding in its own domain. Finally, in the third stage, the algorithm performs interdomain routing if partitioning decision had been made in the first stage. Our simulation results demonstrate that our proposed algorithm yields close performance to the solutions obtained by using the optimization software CPLEX that implements our ILP.