Virtual Network Embedding Algorithm Research Articles

Collaborative Dynamic Virtual Network Embedding Algorithm Based on Resource Importance Measures

Many existing virtual network embedding (VNE) algorithms adopt the greedy embedding strategies, which embed the in-progress virtual network requests (VNRs) onto the substrate nodes and links with more residual resources. However, the previous VNRs may overconsume the critical substrate resources and lead to the resource fragmentation problem in the substrate networks, thus reducing the acceptance ratio of the latter VNRs. To address this problem, we propose a novel collaborative VNE algorithm based on resource importance measures, RIM-ViNE, in which the global node importance and link importance in the initial substrate network are measured using multiple topological attributes and are used to set the embedding cost of VNRs. Then, the VNE problem is defined as a linear programming model and is solved by minimizing the total embedding cost, which could bring about the coordinative embedding between different VNRs, and thus preventing the critical resources from being over occupied by the previous VNRs with small requirements and improving the resource fragmentation problem. Moreover, we propose a dynamic reconfiguration mechanism based on critical nodes protection (CNP-Re) to improve the resource fragmentation problem further. Extensive experiments are conducted under two network scenarios, and the results show that the proposed algorithms outperform the VNE algorithms that only consider the coordination between node mapping and link mapping or only measure the node importance in the residual substrate network, and the average VNR acceptance ratio, average revenue, and average resource utilization ratio are effectively improved.

Virtual Network Embedding Using Node Multiple Metrics Based on Simplified ELECTRE Method

The concept of network virtualization has attracted significant attention from academia to industry. One of the key challenges in network virtualization is the resource allocation problem, which is also termed the virtual network embedding (VNE) problem. It involved with mapping virtual networks onto a substrate network by adhering to some constraints, such as CPU capacity, on the nodes and bandwidth resources on the links. However, prior heuristic VNE algorithms mostly concentrate on measuring the embedding potential of substrate nodes using the multiplication of different nodes’ resource metrics. Due to the fact that different resource metrics have different impacts on node ranking, these traditional methods have some limitations that would cause unbalanced embedding problems. Furthermore, the number of hops for the substrate paths that virtual links are mapped onto will have a large impact on the resource utilization of substrate links in a substrate network. In this paper, based on the topology analysis of six situations, we first propose a novel five-node ranking metric to quantify the importance of substrate nodes. Then, we give a comprehensive measurement method for substrate nodes using the simplified ELECTRE method to avoid an unbalanced embedding solution. We present a novel two-stage VNE algorithm, which chooses the substrate nodes with the maximum embedding potential to perform the node mapping procedure, and uses the shortest path algorithm to accomplish the link mapping procedure. Extensive simulation results demonstrated that our proposed method behaves better than the other state-of-the-art algorithms in terms of the long-term average revenue, the revenue-to-cost (R/C) ratio, and the VN request acceptance ratio.

ER-VNE: A Joint Energy and Revenue Embedding Algorithm for Embedding Virtual Networks

Network virtualization (NV) has been accepted as the integral paradigm of next generation network (e.g., 5G virtualized network) since its inception. Virtual network embedding (VNE) is the resource optimization problem for NV. Over the past decade, multiple VNE algorithms have been proposed. However, prior VNE algorithms focus on embedding more proposed virtual networks (VNs) onto the shared substrate networks (SNs) so as to maximize embedding revenues of internet service providers (ISPs), managing and operating the shared SNs. Due to the fact that energy cost is approaching more than half of the operating cost of shared SNs, it is crucial for ISPs to minimize total VN energy cost so as to maximize the net profit. To deal with this issue, a formal VNE problem model and VNE energy cost model are first proposed. Then, a novel node ranking approach is proposed, jointly quantifying the multiple energy and revenue related topological attributes. The novel node ranking approach is able to compute stable node embedding ability. Next, an energy efficient heuristic algorithm ( ER-VNE ) is detailed. Numerical simulations are made to validate that the ER-VNE can significantly reduce the energy cost by approximately 16% over the typical energy-related algorithm while embedding the same amount of VNs successfully.

A Virtual Network Embedding Algorithm Based on Cellular Automata Genetic Mechanism

The optimal embedding problem of virtual network requests, which satisfies nodes and link constraints, is a NP-hard problem. Heuristic algorithms solve the problem with the mathematical model optimization, but it fails to consider the influence of the virtual network embedding node itself on the optimal solution. So the cellular automata genetic mechanism is introduced into the problem, and the virtual network embedding algorithm based on cellular genetic algorithm (VNE-CGA) has been proposed. VNE-CGA uses the cellular automata to model the node, and replaces the "B4567/S1234" rule with the crossover operation in genetic algorithm. Through learning from neighbours to guide the individual's optimization process, VNECGA improves the inherent defects of traditional genetic algorithm. The experimental results show that the request acceptance ratio and the long-term average revenue increase about 5% and 12%.

Survivable Virtual Network Link Protection Method Based on Network Coding and Protection Circuit

Network virtualization is believed to be a promising way for the next generation Internet to overcome network ossification. It allows multiple heterogeneous virtual networks (VNs) to be embedded onto the shared substrate network (SN) to offer more flexibility and better manageability. With the increasing deployments of VNs in a variety of networks, how to protect the VNs against the single substrate link failure has become a key challenge. In this paper, we propose a survivable VN link protection method based on network coding and protection circuit. First, we provide an integer linear programming formulation for the survivable VN link protection to maximize the long-term average revenue to cost ratio. Second, a novel protection circuit technology is introduced to augment the VN at the VN level to reduce the backup resource consumption and provide more flexible VN management. Then, an efficient heuristic virtual network embedding algorithm is developed, which makes full use of the limited resources and transforms the single substrate link protection into the single virtual link protection in multiple protection circuits. Finally, the data units from different links are encoded using network coding, which not only provides 1 + N protection to virtual links but also reduces the recovery delay. The evaluation results show that our method not only has the best acceptance ratio and long-term average revenue to cost ratio, but it also greatly enhances the achievable backup sharing and shortens the average network recovery delay.

Virtual network embedding based on modified genetic algorithm

To fend off the ossification of Internet architecture, virtual network embedding has been propounded as one of the most important techniques to address this issue. Virtual network embedding is a process that consists of two stages including node mapping stage and link mapping stage, the aim of node mapping stage is to map the virtual nodes from virtual network requests (VNRs) onto the substrate nodes meanwhile satisfying the CPU capacity constraints on nodes, the goal of link mapping stage is to map the virtual links from VNRs onto the substrate paths while satisfying the bandwidth resource constraints on links. This paper proposed a virtual network embedding algorithm based on modified genetic algorithm, improved the classical genetic algorithm from three aspects: population initialization strategy, improved mutation operation and improvement operation, took advantage of the selection operation, crossover operation, mutation operation, feasibility checking operation, and utilized the fitness function to choose the best chromosome. Simulation results indicated that our proposed method has significantly increased the acceptance ratio of VNRs and the long-term average revenue of Infrastructures (InPs) compared with other two state-of-the-art algorithms.

Efficient virtual network embedding algorithm based on restrictive selection and optimization theory approach

Network virtualization (NV) is widely considered as a key component of the future network and promises to allow multiple virtual networks (VNs) with different protocols to coexist on a shared substrate network (SN). One main challenge in NV is virtual network embedding (VNE). VNE is a NP-hard problem. Previous VNE algorithms in the literature are mostly heuristic, while the remaining algorithms are exact. Heuristic algorithms aim to find a feasible embedding of each VN, not optimal or sub-optimal, in polynomial time. Though presenting the optimal or sub-optimal embedding per VN, exact algorithms are too time-consuming in small-scaled networks, not to mention moderately sized networks. To make a trade-off between the heuristic and the exact, this paper presents an effective algorithm, labeled as VNE-RSOT (Restrictive Selection and Optimization Theory), to solve the VNE problem. The VNE-RSOT can embed virtual nodes and links per VN simultaneously. The restrictive selection contributes to selecting candidate substrate nodes and paths and largely cuts down on the number of integer variables, used in the following optimization theory approach. The VNE-RSOT fights to minimize substrate resource consumption and accommodates more VNs. To highlight the efficiency of VNE-RSOT, a simulation against typical and state-of-art heuristic algorithms and a pure exact algorithm is made. Numerical results reveal that virtual network request (VNR) acceptance ratio of VNE-RSOT is, at least, 10% higher than the best-behaved heuristic. Other metrics, such as the execution time, are also plotted to emphasize and highlight the efficiency of VNE-RSOT.

An evaluation method for secure virtual network embedding algorithms

Due to the ossification of the current Internet, it is difficult to launch new service. One of solutions is network virtualization. Numerous virtual network embedding (VNE) algorithms have been proposed in many literatures. But, there is no general methods or frameworks for evaluation of these algorithms. We have analyzed a number of studies, and found appropriate evaluation indexes that can be used in evaluating the functionalities of VNE algorithms. Based on those indexes, we presented a new evaluation method of secure VNE algorithms. To make a virtual network with the various requirements, the infrastructure provider needs effective resource allocation algorithm. The role of VNE is to allocate physical resources to virtual nodes or links to form virtual networks. In order to use the resources of physical networks, appropriate resource allocation algorithms are required. We found a set of evaluation indexes by analyzing the previous proposed researches. Through analysis, we found that our proposed method can be grouped into two functional attributes for classification. One attribute is the basic attributes that mean special features of architecture and the other is the evaluation attributes that perform the assessment of algorithms. We have evaluated the algorithms with our proposed evaluation methods and found to be useful to choose the appropriate algorithm to the infrastructure provider. The proposed method was found to be more convenient to perform the evaluation of the algorithms in real-world simulation. This method helps infrastructure providers to choose the appropriate VNE algorithm.

Clustering-based energy-aware virtual network embedding

Virtual network embedding has received a lot of attention from researchers. In this problem, it needs to map a sequence of virtual networks onto the physical network. Generally, the virtual networks have topology, node, and link constraints. Prior studies mainly focus on designing a solution to maximize the revenue by accepting more virtual networks while ignoring the energy cost for the physical network. In this article, to bridge this gap, we design a heuristic energy-aware virtual network embedding algorithm called EA-VNE-C, to coordinate the dynamic electricity price and energy consumption to further optimize the energy cost. Extensive simulations demonstrate that this algorithm significantly reduces the energy cost by up to 14% over the state-of-the-art algorithm while maintaining similar revenue.

Virtual Network Embedding based on Node Connectivity Awareness and Path Integration Evaluation

As a main challenge in network virtualization, virtual network embedding problem is increasingly important and heuristic algorithms are of great interest. Aiming at the problems of poor correlation in node embedding and link embedding, long distance between adjacent virtual nodes and imbalance resource consumption of network components during embedding, we herein propose a two-stage virtual network embedding algorithm NA-PVNM. In node embedding stage, resource requirement and breadth first search algorithm are introduced to sort virtual nodes, and a node fitness function is developed to find the best substrate node. In link embedding stage, a path fitness function is developed to find the best path in which available bandwidth, CPU and path length are considered. Simulation results showed that the proposed algorithm could shorten link embedding distance, increase the acceptance ratio and revenue to cost ratio compared to previously reported algorithms. We also analyzed the impact of position constraint and substrate network attribute on algorithm performance, as well as the utilization of the substrate network resources during embedding via simulation. The results showed that, under the constraint of substrate resource distribution and virtual network requests, the critical factor of improving success ratio is to reduce resource consumption during embedding.

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Special issue on Big Data Security and Intelligent Data in Clouds (<scp>BDS‐IDC</scp>)

A Markov Reward based Resource-Latency Aware Heuristic for the Virtual Network Embedding Problem

An ever increasing use of virtualization in various emerging scenarios, e.g.: Cloud Computing, Software Defined Networks, Data Streaming Processing, asks Infrastructure Providers (InPs) to optimize the allocation of the virtual network requests (VNRs) into a substrate network while satisfying QoS requirements. In this work, we propose MCRM, a two-stage virtual network embedding (VNE) algorithm with delay and placement constraints. Our solution revolves around a novel notion of similarity between virtual and physical nodes. To this end, taking advantage of Markov Reward theory, we define a set of metrics for each physical and virtual node which captures the amount of resources in a node neighborhood as well as the degree of proximity among nodes. By defining a notion of similarity between nodes we then simply map virtual nodes to the most similar physical node in the substrate network. We have thoroughly evaluated our algorithm through simulation. Our experiments show that MCRM achieves good performance results in terms of blocking probability and revenues for the InP, as well as a high and uniform utilization of resources, while satisfying the delay and placement requirements.

COVE: Co-operative Virtual Network Embedding for Network Virtualization

Network virtualization provides a promising solution for next-generation network management by allowing multiple isolated and heterogeneous virtual networks to coexist and run on a shared substrate network. A long-standing challenge in network virtualization is how to effectively and efficiently map these virtual nodes and links of heterogeneous virtual networks onto specific nodes and links of the shared substrate network, known as the Virtual Network Embedding (VNE) problem. Existing centralized VNE algorithms and distributed VNE algorithms both have advantages and disadvantages. In this paper, a novel cooperative VNE algorithm is proposed to coordinate centralized and distributed algorithms and unite their respective advantages and specialties. By leveraging the learning technology and topology decomposition, autonomous substrate nodes entrusted with detailed mapping solutions cooperate closely with the central controller with a global view and in charge of general management to achieve a successful embedding process. Besides a topology-aware resource evaluation mechanism and customized mapping management policies, Bloom filter is elaborately introduced to synchronize the mapping information within the substrate network, instead of flooding which generates massive communication overhead. Extensive simulations demonstrate that the proposed cooperative algorithm has acceptable and even better performance in terms of long-term average revenue and acceptance ratio than previous algorithms.

Virtual Network Embedding via Monte Carlo Tree Search.

Network virtualization helps overcome shortcomings of the current Internet architecture. The virtualized network architecture enables coexistence of multiple virtual networks (VNs) on an existing physical infrastructure. VN embedding (VNE) problem, which deals with the embedding of VN components onto a physical network, is known to be -hard. In this paper, we propose two VNE algorithms: MaVEn-M and MaVEn-S. MaVEn-M employs the multicommodity flow algorithm for virtual link mapping while MaVEn-S uses the shortest-path algorithm. They formalize the virtual node mapping problem by using the Markov decision process (MDP) framework and devise action policies (node mappings) for the proposed MDP using the Monte Carlo tree search algorithm. Service providers may adjust the execution time of the MaVEn algorithms based on the traffic load of VN requests. The objective of the algorithms is to maximize the profit of infrastructure providers. We develop a discrete event VNE simulator to implement and evaluate performance of MaVEn-M, MaVEn-S, and several recently proposed VNE algorithms. We introduce profitability as a new performance metric that captures both acceptance and revenue to cost ratios. Simulation results show that the proposed algorithms find more profitable solutions than the existing algorithms. Given additional computation time, they further improve embedding solutions.

Fair Virtual Network Embedding Algorithm with Topology Pre-Configuration

Fair Virtual Network Embedding Algorithm with Topology Pre-Configuration

A Efficient Mapping Algorithm With Novel Node-Ranking Approach for Embedding Virtual Networks

Virtual network embedding (VNE) problem has been widely accepted as an important aspect in network virtualization (NV) area: how to efficiently embed virtual networks, with node and link resource demands, onto the shared substrate network that has finite network resources. Previous VNE heuristic algorithms, only considering single network topology attribute and local resources of each node, may lead to inefficient resource utilization of the substrate network in the long term. To address this issue, a topology attribute and global resource-driven VNE algorithm ( VNE-TAGRD ), adopting a novel node-ranking approach, is proposed in this paper. The novel node-ranking approach, developed from the well-known Google PageRank algorithm, considers three essential topology attributes and global network resources information before conducting the embedding of given virtual network request (VNR). Numerical simulation results reveal that the VNE-TAGRD algorithm outperforms five typical and latest heuristic algorithms that only consider single network topology attribute and local resources of each node, such as long-term average VNR acceptance ratio and average revenue to cost ratio.

Optimal virtual network embedding based on artificial bee colony

As one of the key challenges in network virtualization, the problem of virtual network embedding has attracted significant attention from researchers. In this problem, it needs to embed virtual networks with both node and link demands into a shared physical network. The main goal of this problem is to embed more virtual networks to gain more revenue. However, the prior approaches still suffer from low performance and await to be further optimized in terms of this goal. In this paper, we design an artificial bee colony-based virtual network embedding algorithm, called VNE-ABC, to solve this problem. The core idea of this algorithm is to leverage the iterations and intelligence of artificial bee colony to achieve a more optimal solution for virtual network embedding. Through simulations, we show that our proposed algorithm gains about 35.4% more revenue than the existing algorithm.

Virtual network embedding with pre‐transformation and incentive convergence mechanism

SummaryEfficient and fair resource allocation for multitudinous virtual networks running cloud‐based applications is crucial to archive dynamic resources multi‐tenancy in cloud computing. In order to solve the problem, we propose a novel virtual network embedding (VNE) algorithm to increase revenue and utilization of substrate network as well as to improve acceptance fairness of virtual networks. First, we present a virtual topology pre‐transformation mechanism leveraging reusable technology to reduce topology difference and achieve acceptance fairness. Then, because of the Non‐deterministic polynomial‐time (NP)‐hard characteristics of VNE, we model the problem as an integer linear programming problem and solve the VNE problem with a discrete particle swarm optimization‐based algorithm. The operations and parameters of particles are well redefined according to the VNE context. Finally, an incentive convergence mechanism is proposed to reduce mapping complexity, which can be used to accelerate convergence and to save more bandwidth by exploiting individual candidate nodes' lists. Simulation results prove that our proposed method is superior to the existing similar algorithms in terms of physical resource utilization, acceptance fairness, revenue/cost ratio, and searching efficiency. Copyright © 2016 John Wiley &amp; Sons, Ltd.

An exact virtual network embedding algorithm based on integer linear programming for virtual network request with location constraint

Network virtualization is known as a promising technology to tackle the ossification of current Internet and will play an important role in the future network area. Virtual network embedding(VNE) is a key issue in network virtualization. VNE is NP-hard and former VNE algorithms are mostly heuristic in the literature. VNE exact algorithms have been developed in recent years. However, the constraints of exact VNE are only node capacity and link bandwidth. Based on these, this paper presents an exact VNE algorithm, ILP-LC, which is based on Integer Linear Programming(ILP), for embedding virtual network request with location constraints. This novel algorithm is aiming at mapping virtual network request(VNR) successfully as many as possible and consuming less substrate resources. The topology of each VNR is randomly generated by Waxman model. Simulation results show that the proposed ILP-LC algorithm outperforms the typical heuristic algorithms in terms of the VNR acceptance ratio, at least 15%.

Presto: Towards efficient online virtual network embedding in virtualized cloud data centers

As an efficient solution to diversify the future Internet for resource sharing in data centers, the network virtualization enables seamless integration of network experiments, services and architectures with different features by allowing multiple heterogeneous virtual networks (VNs) to simultaneously coexist on a shared substrate infrastructure. Embedding multiple virtual networks onto a shared substrate by allocating substrate resources to virtual nodes and virtual links of VN requests under a collection of constrains is known to be an NP-hard problem even for the offline VN embedding. To deal with this issue, this paper formulates the VN embedding problem as a new multiple objective linear programming optimization program, and solves it in a preemptive strategy by decomposing the problem into node mapping and link mapping phases. Furthermore, based on an Artificial Intelligence resource abstraction model, named Blocking Island (BI), we propose an efficient online heuristic VN embedding algorithm called Presto. Presto operates with quite low computation complexity and greatly reduces the search space, which far outperforms other candidates. The goal of Presto is to maximize the economic revenue of infrastructure providers while minimizing the embedding cost. The extensive simulation results further prove the feasibility and good performance of Presto in revenue, VN request acceptance ratio, computation efficiency and resource utilization.