基于网络单纯形的虚拟网络映射算法

在软件定义网络(SDN)架构中,虚拟网络映射是实现网络虚拟化的关键技术。针对虚拟网络映射算法映射成本高、执行时间长的问题,提出一种虚拟网络映射算法Simplex-VNM。在节点映射阶段,对虚拟节点按照资源需求进行排序,综合考虑节点连通性和映射成本选择映射节点。在链路映射阶段,采用网络单纯形算法求解最小费用流问题。实验结果表明,相比于NA-PVNM和Improved-vnmFlib算法,该算法具有更低的映射成本和更短的运行时间。     

基于AWGR的动态光网络及拓扑映射

随着数据中心规模的越来越大,同一个虚拟拓扑中虚拟节点所映射到的物理节点间的距离越来越远,其链路在映射过程中需要经过若干的跳步,占用了大量的物理网络资源,降低了数据中心的收益.受到数据中心固定拓扑的限制,仅通过映射算法的优化很难取得较好的性能和收益提升,因此提出一种基于AWGR的动态光网络和对应的虚拟拓扑映射方法,通过结构和算法的协同提高了数据中心的利用率,从而提高了数据中心的收益.     

基于离散粒子群的节点可重用虚拟网络映射算法

虚拟网络映射问题是网络虚拟化要解决的重点问题,也是云计算环境下实现资源多租赁运营的技术基础。现有的映射算法在计算效率上有待提高,不能充分利用可重用技术以节省网络带宽资源。提出一种可重用的虚拟网络映射算法,首先构建以提高底层物理网络利用率为目标的资源优化分配模型;然后再充分利用可重用技术以内存交换替代网络交换并针对效率问题设计增强的粒子初始位置分配算法,进而通过离散粒子群算法对优化问题进行求解。仿真实验结果表明,提出的算法相较已有的普通粒子群算法在物理网络收益上有显著提高,增强的初始位置分配机制也有助于计算效率的提升。     

基于两次优先级排序的虚拟网络映射算法

为解决现有的虚拟网络映射算法忽略网络本身属性,仅按照请求到达的顺序分配资源而导致物理资源利用率低的问题,利用时间窗模型,提出了基于两次优先级排序的虚拟网络映射算法。在第一次排序中,粗化虚拟网络请求的同时根据业务类型、属性参数计算请求优先级,初步确定窗口中虚拟网络映射顺序;在第二次排序中,综合考虑链路带宽资源需求和节点途径跳数,通过链路权重来确定优先级,计算最佳映射路径。仿真结果表明,该算法降低了虚拟网络请求的平均等待时间,提高了请求接受率及收益开销比。     

基于熵权VIKOR的安全虚拟网络映射算法

近年来,虚拟网络映射技术作为网络虚拟化的关键技术,成为学术界与工业界研究的重点之一。针对安全虚拟网络映射中因节点安全感知不全面、匹配不合理导致的映射性能较低问题,文章提出了一种基于熵权折衷排序法(VIKOR)的安全虚拟网络映射算法。该算法首先将安全虚拟网络映射问题构建为混合整数线性规划模型,设计了节点安全优先度指标,实现了虚拟网络节点与底层网络节点安全联合感知;其次在映射过程中综合考虑节点资源属性、拓扑属性和安全属性,采用熵权VIKOR进行节点排序;最后按照节点排序结果依次进行映射,其中链路映射采用k最短路径算法。仿真结果表明,在满足节点各项约束的前提下,文章算法提高了虚拟网络映射成功率和收益开销比。     

基于多目标粒子群优化的虚拟网络映射算法

在虚拟网络映射中,多数研究只考虑一个映射目标,不能体现多方的利益。为此,将多目标算法和粒子群算法结合,提出了一种基于多目标粒子群优化（PSO）的虚拟网络映射算法（VNE-MOPSO）。首先,在基本的粒子群算法中引入交叉算子,扩大了种群优化的搜索空间;其次,在多目标优化算法中引入非支配排序、拥挤距离排序,从而加快种群的收敛;最后,以同时最小化成本和节点负载均衡度为虚拟网络映射目标函数,采用多目标粒子群优化算法求解虚拟网络映射问题（VNMP）。实验结果表明,采用该算法求解虚拟网络映射问题,在网络请求接受率、平均成本、平均节点负载均衡度、基础设施提供商的收益等方面具有优势。     

基于实时拓扑属性的虚拟网络映射算法（英文）

研究目的:基于虚拟网络请求和底层物理网络实时拓扑属性,提出一种高效的两步式虚拟网络映射算法。创新要点:分别利用中介中心性和物理节点相关性对虚拟网络请求和底层物理网络中节点进行重要性评估,在此基础上给出一种两步式映射算法(算法1,2)。研究方法:首先给出中间中心性、接近中心性以及节点相关性计算模型,结合节点本地资源分别提出虚拟网络请求和物理网络中节点排名计算方式。当虚拟网络请求到达后,根据虚拟节点排名,将其映射到拥有足够资源的物理节点中排名最靠前的节点。节点映射完成后,使用K-th最短路径算法进行链路映射。映射过程中采用文献(Yu et al.,2008)中所使用的时间窗口模式进行接入控制。重要结论:利用节点本地资源,针对性分析虚拟网络请求和物理网络实时拓扑属性,提出两步式映射算法。该算法提高请求接受率、开销收益比的同时减少算法映射时间,取得更好的映射效果(图3-10)。     

基于拓扑分割与聚类分析的虚拟软件定义网络映射算法

针对目前大部分基于虚拟软件定义网络（vSDN）的映射算法未充分考虑节点与链路之间的相关性的问题,提出了一种基于网络拓扑分割与聚类分析的vSDN映射算法。首先,通过根据最短跳数进行拓扑分割的方法,降低物理网络的复杂度;然后,通过根据节点拓扑和资源属性进行聚类分析的方法,提升映射算法的请求接受率;最后,通过将链路约束分散到节点带宽资源以及节点的度进行约束考量,对不符合链路要求的节点进行重映射,从而优化了节点与链路映射过程。实验结果表明,该算法有效地提升了基于软件定义网络（SDN）架构的虚拟网络映射算法在较低连通概率物理网络下的请求接受率。     

一种基于约束优化的虚拟网络映射方法

虚拟网络映射问题将不同的虚拟网络应用映射到相同的基础设施网络中,这是一个极具挑战性的问题.针对该问题,提出了一种基于约束优化的虚拟网络映射方法,将映射问题分解为节点映射和链路映射两个阶段,其中,前者是将虚拟节点映射到物理节点上,后者将虚拟链路映射到物理路径上,它们都是NP难问题.针对节点映射和链路映射分别提出了node-mapping算法和link-mapping算法.node-mapping算法基于贪婪算法的思想,映射时考虑了物理节点所能提供的资源数量以及物理节点间距离两个因素,该算法能够保证基础设施网络中各节点间的负载相对均衡;同时,通过采用访问控制机制,过滤一些异常的虚拟网络请求,能够有效地提高资源的使用效率.link-mapping算法基于人工智能领域中的分布式约束优化思想,其能够保证得到的解是全局最优的,即映射链路的代价最小.最后,通过模拟实验对该方法进行验证,实验结果表明该方法在求解虚拟网络映射问题时的性能良好.     

节点与链路协同映射的生存性虚拟光网络映射算法

针对弹性光网络中单链路故障问题,提出一种基于匈牙利算法求解链路映射方案的节点与链路协同映射保护算法CMST-HA。将虚拟网络请求的节点与链路分别划分为主动类型与被动类型,把主动类型节点映射至邻接链路频谱资源丰富且邻接节点计算资源充足的物理节点上,在主动链路时使用匈牙利算法求解出最小映射开销方案并完成映射,确定被动节点的映射位置,利用KSP算法为被动链路选择映射路径,在此基础上为虚拟网络请求的最小生成树链路提供备份路径。仿真结果表明,与RVNM、CMST算法相比,CMST-HA算法不仅能够降低虚拟网络请求阻塞率,而且可增加物理网络收益。     

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.     

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.     

基于负载均衡的无线虚拟网络映射算法

针对网络僵化的问题,目前多采用网络虚拟化（NV）方法进行解决,其关键技术是虚拟网络映射（VNE）。为解决无线VNE过程中功率和带宽资源使用不均衡的问题,基于负载均衡原理提出一种联合资源分级的无线VNE算法。首先,采用新的节点资源排序方式,其中将节点功率和平均链路带宽作为排序依据;其次,对资源进行分级,以动态调整虚拟网络请求对功率和带宽资源的需求;最后,改进功率和带宽资源的单位成本,并以最小化成本为目标函数选择资源分配方案。与原有的无线VNE算法WVNE-JBP相比,所提算法的总体接受率提高了11.7个百分点,平均功率利用率提高了4.4个百分点,平均带宽利用率提高了1.6个百分点。实验结果表明,所提算法能有效提高虚拟网络接受率和资源利用率。     

LIVE: Learning and Inference for Virtual Network Embedding

Network virtualization provides a promising tool for next-generation network management by allowing multiple heterogeneous virtual networks to run on a shared substrate network. A long-standing challenge in network virtualization is how to effectively map these virtual networks onto the shared substrate network, known as the virtual network embedding (VNE) problem. Most heuristic VNE algorithms find practical solutions by leveraging a greedy matching strategy in node mapping. However, greedy node mapping may lead to unnecessary bandwidth consumption and increased network fragmentation because it ignores the relationships between the mapped virtual network requests and the mapping ones. In this paper, we re-visit the VNE problem from a statistical perspective and explore the potential dependencies between every two substrate nodes. We define a well-designed dependency matrix that represents the importance of substrate nodes and the topological relationships between them, i.e., every substrate node’s degree of belief. Based on the dependency matrix generated from collecting and processing records of accepted virtual network requests, Bayesian inference is leveraged to iteratively select the most suitable substrate nodes and realize our novel statistical VNE algorithm consisting of a learning stage and an inference stage in node mapping. Due to the overall consideration of the relationships between the mapped nodes and the mapping ones, our statistical approach reduces unnecessary bandwidth consumption and achieves a better performance of embedding. Extensive simulations demonstrate that our algorithm significantly improves the long-term average revenue, acceptance ratio, and revenue/cost ratio compared to previous algorithms.     

一种启发式网络虚拟化资源分配算法

网络虚拟化是克服当前Internet僵化问题的一种重要方法,而资源分配是网络虚拟化技术的核心.为了平衡负载,本文提出了一种启发式资源分配算法HVNE.该算法充分利用虚拟节点和虚拟链路间的关联因素(虚拟网络拓扑),将节点映射和链路映射两个过程合并为一个统一的过程,改善了传统映射算法在拓扑稀疏时,算法性能不理想的问题.此外,HVNE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,节约了物理链路资源.HVNE将无向图的"k-区域划分优化"理论与传统的拓扑分割理论相结合,定义了虚拟拓扑间节点的关联因子,改进了传统的星形分割方法,使之能适用于大规模网络.仿真实验表明,HVNE在保证网络负载的情况下,获得了较好的虚拟请求接受率,较高的资源利用率和网络收益.     

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.     

A green energy-aware hybrid virtual network embedding approach

In the past few years, the concept of network virtualization has received significant attention from industry and research fora, as it represents a promising way to diversify existing networks and ensure the co-existence of heterogeneous network architectures on top of shared substrates. Virtual network embedding (VNE) is the process of dynamically mapping virtual resources (i.e. virtual nodes and links) onto physical substrate resources. VNE is the main resource allocation challenge in network virtualization and is considered as an NP-hard problem. Several centralized and distributed VNE approaches have been proposed, with the aim of satisfying different objectives ranging from QoS, to economical profit, and network survivability. More recently, emerging VNE approaches started investigating the optimization of new objectives such as energy-efficiency and networks’ security. In this work, we propose a green energy-aware hybrid VNE hybrid VN embedding approach that aims at achieving energy efficiency and resource consolidation, while minimizing CO₂ emissions resulting from VNs operation. This approach consists of a hierarchical virtual networking management architecture in which control and management nodes collaborate for the splitting and embedding of sub-VNs requests to the cleanest substrate resources (i.e. the resources deployed in a sector with the smallest CO₂ emission factor) available. Three different variants of our VNE algorithms, taking into consideration different resources’ selection criteria (i.e. energy source, request priority, and request location) are presented, and their performance is compared with two existing VNE algorithms based on centralized and distributed embedding approaches. The comparative performance analysis shows that our proposed approach enables a more efficient VN embedding in terms of: a reduced number of substrate resources needed, a faster request mapping time, as well as resource consolidation and reduced resource cost. Furthermore, it enables a reduction of the carbon footprint of the VNE operation, thus resulting in a more green and environmentally conscious approach to network virtualization.     

A Global Optimal Mapping Method of Network based on Discrete Optimization Firefly Algorithm

In order to solve the current network rigidity and optimize the resource requirements of multiple virtual networks for synchronous mapping, improve the success rate of virtual network mapping requests, the long-term revenue and overhead ratio of the substrate network, node resource utilization rate and link resource utilization rate. A global optimal mapping method based on discrete optimization firefly algorithm is presented. Analyze the problem of virtual network mapping, map virtual nodes to physical nodes, and map virtual links to physical paths. According to the resource constraints of the virtual network and the substrate network, a multi-objective optimization model of the virtual network mapping is constructed, and the discrete fireflies optimization algorithm is used to obtain the global optimal solution of the virtual network mapping model to achieve the optimal allocation of global resources. The experimental results show that the discrete optimization firefly algorithm has a good performance in solving the virtual network mapping problem, and can effectively improve the virtual network request acceptance rate, node resource utilization rate, link resource utilization rate, and long-term revenue and cost ratio of the substrate network, ensuring Optimization of virtual network resources.     

Performance evaluation of artificial intelligence algorithms for virtual network embedding

Network virtualization is not only regarded as a promising technology to create an ecosystem for cloud computing applications, but also considered a promising technology for the future Internet. One of the most important issues in network virtualization is the virtual network embedding (VNE) problem, which deals with the embedding of virtual network (VN) requests in an underlying physical (substrate network) infrastructure. When both the node and link constraints are considered, the VN embedding problem is NP-hard, even in an offline situation. Some Artificial Intelligence (AI) techniques have been applied to the VNE algorithm design and displayed their abilities. This paper aims to compare the computational effectiveness and efficiency of different AI techniques for handling the cost-aware VNE problem. We first propose two kinds of VNE algorithms, based on Ant Colony Optimization and genetic algorithm. Then we carry out extensive simulations to compare the proposed VNE algorithms with the existing AI-based VNE algorithms in terms of the VN Acceptance Ratio, the long-term revenue of the service provider, and the VN embedding cost.