基于粒子群优化的虚拟网络映射算法

本文以提高底层网络资源利用效率为目标,在底层网络不需要支持路径分裂的情况下,建立了虚拟网络映射问题的整数线性规划模型,并提出了一种新的基于粒子群优化的虚拟网络映射算法.该算法以映射开销作为适应度函数,重新对粒子的参数和相关操作进行了定义.模拟实验结果表明,与已有研究成果相比,该算法显著地提高了底层网络长期平均运营收益与...     

虚拟网络可生存的启发式可靠映射算法

针对虚拟网络可靠映射问题,引入虚拟网络可生存约束条件,确保在底层网络单节点失效情况下,被映射虚拟网络剩余部分仍保持连通,最大程度确保虚拟网络的完整性和服务的连续性。以最小化底层网络映射开销为目标函数,建立虚拟网络可靠映射的整数线性规划模型,提出一种虚拟网络可生存的启发式可靠映射算法RHM-SVN并进行求解。实验结果表明,该算法能够有效降低资源平均利用率,提高映射成功率、底层网络平均收益和虚拟网络恢复成功率。     

区分服务QoP的可生存虚拟网络映射算法研究

用户对虚拟网保护需求呈现多样化的趋势,针对如何实现不同需求的可生存虚拟网映射是当前研究亟待解决的问题。建立了区分保护质量（QoP）的可生存虚拟网络映射问题的数学模型,然后提出了一种启发式算法进行求解。该算法包括广度优先搜索的节点映射策略和区分QoP的链路映射策略。模拟实验表明,该算法不仅能满足用户不同的虚拟网保护需求,而且相比现有算法降低了底层网络资源开销。     

网络切片下基于遗传算法的虚拟网资源分配算法

在网络切片环境下,虚拟网络的资源分配是重要的研究内容.为降低底层网络的开销、提升虚拟网资源分配的成功率,本文提出了基于遗传算法的虚拟网资源分配算法.首先,本文对虚拟网资源分配问题进行建模,并设计了底层网络开销和底层收益两个评价指标.其次,为提升资源分配算法性能,设计了虚拟网节点资源需求规模的评价指标.最后,对遗传算法的关键要素进行定义,并提出了基于遗传算法的虚拟网资源分配算法.本文算法能够按照虚拟网的节点资源需求评估值确定资源分配的优先级,通过智能算法获得全局最优解.在实验部分,通过与传统算法比较,验证了本文算法降低了底层网络开销,提升了底层网络的收益和虚拟网络映射的成功率.     

一种碎片感知的安全虚拟网络重构方法

针对现有的虚拟网络重构算法对物理网络中产生的碎片资源考虑不够周到,导致其对在线虚拟网络映射算法的性能改善不够显著的问题,该文定义了一种网络资源碎片度度量方法,并提出一种碎片感知的安全虚拟网络重构算法。该算法通过周期性考虑物理网络中节点的碎片度,选择出待迁移虚拟节点集合;通过综合考虑物理网络的碎片度减小量和虚拟网络的映射开销减少量,选择出最佳的虚拟节点迁移方案。仿真结果表明,该算法的请求接受率和收益开销比均优于当前的重构算法,特别是在收益开销比方面的优势更加明显。     

虚拟网络映射模型和算法研究

虚拟网络映射是网络虚拟化的关键问题之一,其目的是在满足虚拟网络资源需求的前提下,为该虚拟网络分配合适的底层网络节点和链路资源,从而在共享的物理网络基础设施之上构建彼此隔离的多重异构虚拟网络,为网络基础创新研究提供实验环境和平台,为网络新应用提供承载服务。论述了虚拟网络映射模型和映射算法,并提出基于最小割集理论设计VN映射算法。     

基于节点可靠性感知和共享路径保护的虚拟网映射算法研究

网络虚拟化技术为目前的网络架构提供了一种有效的扩展手段。近年来,底层网络基础设施失效事件频发,因此如何提高虚拟网络的可靠性成为目前该领域一个研究热点。对在保证虚拟网络可靠性的同时如何最小化底层网络映射开销问题进行研究,设计了一个新的启发式算法对其进行求解。实验表明,相比其他算法,所提算法网络带宽资源开销更低。     

基于粒子群优化的负载均衡的虚拟网络映射

虚拟网络映射忽略通信路径上中间节点的资源消耗,以底层物理网络资源消耗最小或路径最短为目标,导致最优路径上中间节点资源不足而出现瓶颈,影响整个底层网络的性能及后续虚拟网络请求成功率。该文针对这一问题考虑物理路径上中间节点资源消耗,以节点负载和链路负载同时达到均衡为目标,将路径跳数限制作为约束,建立虚拟网络映射的数学模型,并采用多目标负载均衡粒子群优化算法求解,记作 LB-PSO。实验证明该算法有效地消除了资源瓶颈,为后续虚拟网络请求提供一个更为均衡的底层物理网络,从而提高了虚拟网络构建成功率、网络资源利用率以及基础设施提供商的收益。     

基于多商品流模型的虚拟链路映射

网络虚拟化是解决网络僵化问题和更好地共享底层网络资源的一种有效途径,虚拟网络映射是实施网络虚拟化的关键问题之～。虚拟网络映射包括节点映射和链路映射两个方面,其目标是为虚拟网络请求分配合适的底层网络节点和链路资源。阐述了底层网络支持路径分割时,如何基于多商品流模型实现VN链路映射。     

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。     

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.     

可重构网络中基于中心度与拓扑势排序的资源分配算法

可重构网络中的一个研究难点是虚拟资源的分配问题。首次将社会网络中心度和拓扑势研究引入到可重构网络虚拟资源分配问题中,在映射虚拟网络时考虑节点在网络中的位置重要性,提出了基于中心度和拓扑势的虚拟网映射算法。实验表明,所提出的新算法在提高资源分配效率,降低物理网络开销方面,与本领域以往算法相比,具有显著的进步。     

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.     

集中与分布:协同虚拟网络映射

结合传统集中式和分布式两类算法各自的特性,提出了协同虚拟网络映射算法。该算法保留了集中式算法中拥有全局视野的中心控制实体,负责总体控制和关键决策,同时将具体映射方案的计算过程交给有限的底层网络子集实现;唯一的中心控制实体与多个底层节点相互配合协作,共同完成虚拟网络映射的整个过程。该算法继承了集中式和分布式算法各自的优势,有效弥补了二者的缺陷,初步的仿真试验也证明了其可行性和有效性。     

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。     

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。     

基于拓扑感知的虚网映射算法框架（英文）

The virtual network embedding/mapping problem is an important issue in network virtualization in Software-Defined Networking(SDN).It is mainly concerned with mapping virtual network requests,which could be a set of SDN flows,onto a shared substrate network automatically and efficiently.Previous researches mainly focus on developing heuristic algorithms for general topology virtual network.In practice however,the virtual network is usually generated with specific topology for specific purpose.Thus,it is a challenge to optimize the heuristic algorithms with these topology information.In order to deal with this problem,we propose a topology-cognitive algorithm framework,which is composed of a guiding principle for topology algorithm developing and a compound algorithm.The compound algorithm is composed of several subalgorithms,which are optimized for specific topologies.We develop star,tree,and ring topology algorithms as examples,other subalgorithms can be easily achieved following the same framework.The simulation results show that the topology-cognitive algorithm framework is effective in developing new topology algorithms,and the developed compound algorithm greatly enhances the performance of the Revenue/Cost(R/C) ratio and the Runtime than traditional heuristic algorithms for multi-topology virtual network embedding problem.     

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.