信任感知的安全虚拟网络映射算法

针对网络虚拟化技术带来的新的安全威胁,将信任关系和信任度引入到虚拟网络资源分配中,量化分析了网络虚拟化环境中的安全问题,构建了安全虚拟网络映射的数学模型,并在映射过程中考虑节点的局部和全局重要性,采用逼近理想排序法方法对节点进行多属性重要度排序,提出了一种信任感知的安全虚拟网络映射算法。仿真结果表明,该算法在满足虚拟网络请求可信需求的条件下,获得了较好的映射成功率、映射收益和资源利用率。     

虚拟网络映射问题研究综述

对虚拟网络映射问题的相关研究工作进行了充分调研,对现有的研究成果进行了深入分析,并且指出了已有研究工作中关键技术的优缺点。最后讨论了可以进一步研究的方向,为虚拟网络映射问题的研究提供参考。     

应用粒子群优化的绿色虚拟网络映射算法

绿色网络是近年来网络技术研究的热点,以节能为优化目标的虚拟网络映射算法成为基于网络虚拟化技术中资源分配研究的重点.提出了应用粒子群优化的绿色虚拟网络映射算法,重定义粒子群优化算法中的参数和粒子进化行为,以关闭底层网络节点和链路数量最多为适应度函数,在较低的算法复杂度条件下,获得绿色虚拟网络映射的最优解.仿真结果表明,与对比算法相比,静态环境下所提算法的运行时间大幅度降低;动态环境下所提算法的节点关闭率、链路关闭率、虚拟网络请求接受率均有所提升,算法运行时间也大大缩短.     

基于链路可靠性的无线虚拟网络分配方法

针对无线网络链路可靠性差影响无线虚拟网络的分配这一问题,提出了一种基于链路可靠性的无线虚拟网络分配算法（WVNEA-LR）。该算法通过物理网络拓扑预处理和允许同一个虚拟请求中的多个虚拟节点映射到同一个物理节点上的方法,提高了Vn构建成功率和节约了物理链路资源。利用Q因子改善了因拓扑分配稀疏时Vn构建成功率低的问题。此外,WVNEA-LR的节点分配为链路分配作了准备,并通过选择可靠性高的承载路径保证了分配后的Vn具有高可靠性。仿真结果表明, WVNEA-LR获得较好的虚拟网络构建成功率、较高的收益成本比和资源利用率。     

基于链路优先的快速协同虚拟网络映射算法

在网络虚拟化环境中,为了减少链路映射成本和提高收益,提出了一种链路优先的协同映射算法。该算法交替映射链路和节点,并使用贪心思想优先将带宽资源需求较大的虚拟链路映射到跳数较少的物理路径上,最终达到降低虚拟网络平均消耗和提高虚拟网络接受率的目的。仿真结果表明,与已有算法相比,所提算法节约了链路映射的成本,提高了虚拟网络接受率和物理网络收益,算法运行时间也有效缩短。     

Dynamic recovery for survivable virtual network embedding

Network virtualization is a promising way to overcome the current ossification of the Intemet. It is essential challenge to find effective, efficient and robust embedding algorithms for recovering virtual network. The virtual network mapping algorithm based on integer programming which was proposed months ago. But it did consider the faults of physical network resources, which is so called survivable virtual network embedding （VNE） problem. Previous strategies for enabling survivability in network virtualization focused on providing protection for the physical network or enhancing the virtual networks by providing backup physical resources in advance, and treated all the physical failures as link failures. In the article, a dynamic recovery method is proposed to solve the survivable virtual network embedding problem based on the integer programming VNE algorithm. The dynamic recovery method doesn＇t need to backup physical resources and it makes more substrate resources which can be used in the embedding. The dynamic recovery process will be activated only when physical failures occur. Different algorithms are used to recovery node and link failures. Simulations show that the method helps to recover almost all of physical failures by finding the substitute nodes and paths, and its performance is very close to that of pure VNE method without considering physical failures.     

Energy efficient virtual network embedding for federated software‐defined networks

In 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.     

A unified enhanced particle swarm optimization‐based virtual network embedding algorithm

Virtual network (VN) embedding is a major challenge in network virtualization. In this paper, we aim to increase the acceptance ratio of VNs and the revenue of infrastructure providers by optimizing VN embedding costs. We first establish two models for VN embedding: an integer linear programming model for a substrate network that does not support path splitting and a mixed integer programming model when path splitting is supported. Then we propose a unified enhanced particle swarm optimization‐based VN embedding algorithm, called VNE‐UEPSO, to solve these two models irrespective of the support for path splitting. In VNE‐UEPSO, the parameters and operations of the particles are well redefined according to the VN embedding context. To reduce the time complexity of the link mapping stage, we use shortest path algorithm for link mapping when path splitting is unsupported and propose greedy k‐shortest paths algorithm for the other case. Furthermore, a large to large and small to small preferred node mapping strategy is proposed to achieve better convergence and load balance of the substrate network. The simulation results show that our algorithm significantly outperforms previous approaches in terms of the VN acceptance ratio and long‐term average revenue. Copyright © 2012 John Wiley & Sons, Ltd.     

分布式环境中基于协商的虚拟网映射算法

网络虚拟化技术可以在共享的底层物理网络上为用户同时提供多种可定制的服务网络。目前的虚拟网映射算法比较依赖于集中式的管理节点,使其在可靠性和适用范围等方面存在诸多问题。为此,提出了一种分布式环境下的虚拟网映射算法,该算法通过多个节点之间的相互协商来完成虚拟网的映射,并且在降低通信开销和缩短虚拟链路的路径长度方面进行了相应改进。实验结果表明,该算法与同类型算法相比,在资源利用率和通信开销方面具有一定的优越性。     

一种超额认购虚拟数据中心的嵌入算法

多租户数据中心环境下,保证云应用性能的一个重要因素是为租户应用提供可保证的通信带宽,这可以通过为每个租户提供一个独占的虚拟数据中心(VDC)来实现.研究了在物理数据中心网络中超额认购数据中心的嵌入问题.相对于一般虚拟数据中心,超额认购虚拟数据中虚拟机之间的流量模式更加复杂,因此首先利用线性规划方程阐述了流量模型及嵌入问题.对于虚拟机嵌入问题,提出了一种具有较低时间复杂度的启发式算法——分组扰动算法.最后,通过仿真实验将分组扰动算法和先前工作中提出的算法以及著名的first-fit进行了比较,实验表明所提算法在降低算法复杂度的同时提高了嵌入成功率.     

Optimal network-aware virtual data center embedding

Recently, the virtual data center embedding (VDCE) problem has drawn significant attention because of a growing need for efficient means of data center resource allocation. By ensuring a set of virtual data center (VDC) integration requests coming from his customers, among the main concern of an infrastructure provider is the maximization of the utilization rate of data center resources and benefits. However, existing VDCE solutions mostly focus on consolidating virtual machines in a single physical data center. Therefore, in this work, we improve the consolidated targets techniques, that consider only the virtual machines integration, by the consideration of network devices and fabrics (e.g., switches and paths/links). We consider new unreleased constraints such as multiple virtual nodes of the same request co-location, and intermediate node requirements when a virtual link is mapped. To address the above problem, in this paper, we propose a binary linear programming-based model, called BLP-VDCE, to solve the VDCE problem with network-aware consideration. This model ensures a simultaneous consolidated embedding of virtual nodes and virtual links. Extensive simulations show that solving the proposed BLP-VDCE model can efficiently embed VDC requests with a high physical resource utilization rate.     

时间和能量感知的贝叶斯虚拟网映射

针对虚拟网的节能映射问题,建立了结合时间和能量感知的虚拟网映射算法。在对节点和路径的评价标准中加入了时间因素,综合考虑了物理资源的运行时间等因素,用概率理论辅助分析了每个虚拟节点的多个可用物理节点被选中的概率。在节点选择阶段,综合考虑底层节点的剩余资源量、CPU资源利用率增量、节点开启情况和是否延长使用时间等因素,并使用条件概率理论辅助分析得到各可用节点的重要性;在链路选择阶段,综合考虑链路开启情况、延长使用时间和链路长度等因素。不仅使虚拟网请求映射在当前较小的节点和链路集合中,而且映射到了延长时间较短的设备上。实验结果表明,与未考虑时间因素的方法相比,该方法能带来更好的性能和更低的能耗。     

A model for virtual network embedding using Artificial Bee Colony

In a network virtualization environment, a significant research problem is that of virtual network embedding. As the network virtualization system is distributed in nature, an effective solution on how to optimally embed a dynamically generated virtual network request on the substrate networks that are owned and managed by multiple infrastructure providers needs proper attention. The problem is computationally hard, and therefore, many approaches, implying heuristics/meta‐heuristics, have been applied for the same. A meta‐heuristic, Artificial Bee Colony algorithm is getting popular due to its robustness toward complex problem solving. A novel approach based on Artificial Bee Colony to address the dynamic virtual network embedding problem in a multiple infrastructure provider scenario is proposed in this work. Bee population is initialized by using a greedy heuristic in which the number of substrate networks together with virtual network requests constructs a bee. Generated solution, in the population, is improvised by using greedy selection that explores a local search method adopted by the bees. In greedy selection, the new candidate source is memorized by the bee if its fitness is better than the fitness of the existing source. The performance study of the proposed model is done by simulation over various metrics such as embedding cost, embedding time, and acceptance ratio. A comparative study is conducted with other nature‐inspired virtual network embedding algorithms on these metrics. The findings affirm that the proposed virtual network embedding approach performs well and produces better results.     

Adaptive scheme based on status feedback for virtual network mapping

Network virtualization is considered as a powerful way to deploy different network architectures and protocols over a shared physical infrastructure.Virtual network(VN)embedding plays an important role in the network virtualization environment.Virtual network embedding is mainly about mapping virtual nodes and virtual links onto the physical network resources efficiently.However,load balancing of substrate network has not received much consideration.This paper proposes an adaptive virtual network mapping algorithm based on status feedback.The main contribution is to embed the virtual network requests according to the current load distribution of substrate network.This adaptive algorithm differentiates the residual bandwidth of substrate links and takes full advantage of the multi-path to improve the load balancing of the substrate network.Simulation experiments show that the proposed algorithm improves the load distribution of substrate network distinctly while reducing the average substrate link stress significantly.     

Research of embedding algorithm for wireless network virtualization

Wireless network virtualization has become one of the promising techniques in fifth generation mobile networks (5G),and the virtual network embedding plays a very important role.A model was formulated to describe the problem and a wireless virtual network embedding algorithm based on joint bandwidth and power allocation (WVNEA-JBP) was put forward,which selected the suitable node and path to serve the data transmission from virtual networks.In order to minimize the overhead while satisfy the higher capacity request,this algorithm considered the impact of interference of wireless links and supports path splitting.A case study was introduced to show how WVNEA-JBP to work.Furthermore,simulation results show that WVNEA-JBP is able to accomplish the efficient utilization of network resources and improve the accept rate of virtual network requests.     

光网络中的一种基于虚拟源的动态多播保护算法

随着各种多播业务的不断出现,如何在网络的光层实现多播业务的可靠性传输已成为光网络研究中的一个热点问题,而多播保护算法是这一问题的核心.目前多播保护算法的研究都是在假设网络所有节点具有多播功能的条件下进行的,然而由于成本的限制,实际光网络往往是一个稀疏多播光网络,即网络中只有部分节点具有多播功能.针对这一问题,提出了一种基于虚拟源的动态多播保护算法.仿真结果表明:该算法不仅能在稀疏多播光网络中满足多播业务可靠传输的要求,还具有较高的网络资源的利用率和较低的时间复杂度.     

面向多节点故障的生存性虚拟网络映射方法

针对网络虚拟化环境中虚拟网络的生存性问题,提出一种面向多节点故障的生存性虚拟网络映射方法。该方法预先在物理网络中按比例留出备份资源并根据拓扑属性构造节点的候选集合,在发生节点故障后从故障节点的候选集合中选取重映射目标并动态分配预留的备用资源来恢复受到影响的虚拟网络,提高长期运营利润。仿真实验在多节点故障模型下从适用性、稳定性及性能方面验证了该方法的有效性。     

基于遗传算法的大规模企业网拓扑设计新算法

企业网拓扑结构设计中的子网划分问题实际上是一个多目标非线性数学规划问题,选取布线距离、流量分配、流量均衡和传输时延作为子网划分的主要制约因素,建立了相应的数学模型并通过遗传算法求解待优化的目标函数以完成子网划分。分析结果表明,此方法能较好地解决大规模网络拓扑设计中的子网划分问题。     

VNE-AFS：基于人工鱼群的网络虚拟化映射算法

虚拟网络资源映射是云计算研究领域的一个难点问题。以降低底层网络映射开销为目标,提出一种基于人工鱼群的网络虚拟化映射算法VNE-AFS。根据虚拟网络请求对底层网络节点和链路的约束关系建立二进制组合优化模型,并利用人工鱼群算法实现虚拟网络资源向底层网络资源的近似最优映射。实验结果表明,与现有的虚拟网络映射算法相比,该算法有效地降低了底层网络的开销和求解时间,提高了虚拟网络映射的成功率、平均收益和资源利用率。     

一种基于主-备份节点同步映射的VNF热备份机制

网络功能虚拟化实现了网络功能与专属硬件平台的分离,虚拟网络功能（Virtual Network Function,VNF）通过动态组合形成服务功能链,实现同一硬件平台部署不同的功能服务。针对软件定义网络功能服务可靠性和物理网络资源效率问题,提出了一种基于主备份节点同步映射的VNF节点热备份机制。首先,选择满足节点资源约束的物理节点为主备份节点候选映射对;然后,选择满足带宽约束的最短物理链路为服务路径,实现节点与链路资源总开销的优化。实验结果表明,与传统的备份机制相比,该方法能够满足电信级高可靠性的服务需求,降低了VNF节点备份资源总开销,提高了服务请求的接受率和底层物理网络资源的利用率。