A virtual network mapping algorithm based on integer programming

The virtual network （VN） embedding/mapping problem is recognized as an essential question of network virtualization. The VN embedding problem is a major challenge in this field. Its target is to efficiently map the virtual nodes and virtual links onto the substrate network resources. Previous research focused on designing heuristic-based algorithms or attempting two-stage solutions by solving node mapping in the first stage and link mapping in the second stage. In this study, we propose a new VN embedding algorithm based on integer programming. We build a model of an augmented substrate graph, and formulate the VN embedding problem as an integer program with an objective function and some constraints. A factor of topology-awareness is added to the objective function. The VN embedding problem is solved in one stage. Simulation results show that our algorithm greatly enhances the acceptance ratio, and increases the revenue/cost （R/C） ratio and the revenue while decreasing the cost of the VN embedding problem.     

Network virtualization substrate with parallelized data plane

Network virtualization provides the ability to run multiple concurrent virtual networks over a shared substrate. However, it is challenging to design such a platform to host multiple heterogenous and often highly customized virtual networks. Not only high degree of flexibility is desired for virtual networks to customize their functions, fast packet forwarding is also required. This paper presents PdP, a flexible network virtualization platform capable of achieving high speed packet forwarding. A PdP node has multiple machines to perform packet processing for virtual networks hosted in the system. To forward packets in high speed, the data plane of a virtual network in PdP can be allocated with multiple forwarding machines to process packets in parallel. Furthermore, a virtual network in PdP can be fully customized. Both the control plane and data plane of a virtual network run in virtual machines so as to be isolated from other virtual networks. We have built a proof-of-concept prototyping PdP platform using off-the-shelf commodity hardware and open source software. The performance evaluation results show that our system can closely match the best-known packet forwarding speed of software router running in commodity hardware.     

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.     

Novel methods for virtual network composition

Network virtualization has been proposed as a technology that aims to solve the Internet ossification. Central to the network virtualization is a virtual network composition mechanism providing an efficient mapping of virtual nodes and links onto appropriate physical resources in the network infrastructure.This paper proposes a novel backtracking heuristic algorithm for virtual network composition. Based on this algorithm, two approaches with two different objectives are presented. The first approach (Backtracking-CR) aims to compose a virtual network using the least amount of network resources, while the second (Backtracking-LB) applies load balancing for virtual network composition. Furthermore, a linear programming approach that optimizes the virtual network composition with an objective of using the least amount of network resources is presented and used to bench mark the heuristic algorithm. Simulation results show that using less network resources by applying linear programming or Backtracking-CR does not produce higher number of successfully mapped virtual networks when is compared to load balancing approach. Results also show that the proposed heuristic algorithm is scalable to large physical and virtual networks with respect to the computation time.     

网络功能虚拟化环境中大规模资源状态监测策略

在网络功能虚拟化（NFV）环境中,为了提高网络中基础设施资源利用率,高效动态部署服务功能链,编排管理域需要对网络中底层资源及虚拟网络功能状态进行实时监测,但实时监测会产生大量通信开销。提出了网络通信开销最小化的智能分布式监测策略,通过改进的标签传播算法智能划分子网并选择代理监测节点,实现了对资源和虚拟功能状态的高效监测,并使监测信息通信开销最小。仿真结果表明,所提监测策略使网络中监测信息通信开销降低约13%。图4 不同算法下子网节点数量方差图     

面向SDN/NFV环境的网络功能策略验证

SDN与NFV技术带来了网络管理的灵活性与便捷性,但SDN的动态转发策略可能导致网络功能策略失效,同时不同网络功能的策略可能互相影响,引起冲突问题.为了在基于SDN/NFV的云网络中对网络功能的策略进行验证,分析了网络功能与SDN设备之间、跨网络功能之间的策略冲突,建立了统一策略表达进行策略解析,设计策略验证方案、框架...     

有线网络中虚拟网络映射算法研究

网络虚拟化是未来网络的关键技术之一,有助于克服当前网络的“僵化”问题,能够在无需对当前网络架构做出巨大改变的基础上配置新的网络协议和服务,实现多个虚拟网络共存于一个物理网络上,由此产生了新的问题,如何将有限的物理资源合理分配给不同的虚拟网络,即虚拟网络映射问题。根据网络环境,可以分为有线网络和无线网络下的虚拟网络映射。其中,有线网络下的映射是研究虚拟网络映射问题的基础和重点,已有大量算法提出。为了给该问题的研究提供一个全面的视野,从问题定义、存在挑战、映射目标方面对有线网络中虚拟网络映射算法进行综述,根据算法的不同特点进行分类,重点介绍几种典型的算法并进行比较总结,最后指出未来的研究趋势。     

面向动态虚拟网络请求的虚拟网络映射算法

针对虚拟网络请求资源动态变化的实际情况,提出了面向动态虚拟网络请求的虚拟网络映射（DVNR-VNE）算法。以混合线性规划理论为基础,采用多队列的方式分别对不同类型的虚拟网络请求进行预处理,建立了以最小化映射代价和最小迁移代价为优化目标的映射模型,优先映射需要释放资源的请求以获得更多的资源支持其他的虚拟网络,对新到来的虚拟网络请求采用优化后的虚拟网络映射（WD-VNE）算法进行映射。仿真实验表明,该算法降低了链路映射成本和迁移成本并获得了较高的虚拟网络请求接受率。     

Performance improvement for applying network virtualization in fiber-wireless （FiWi） access networks

Fiber-wireless （FiWi） access networks, which are a combination of fiber networks and wireless networks, have the advantages of both networks, such as high bandwidth, high security, low cost, and flexible access. However, with the increasing need for bandwidth and types of service from users~ FiWi networks are still relatively incapable and ossified. To alleviate bandwidth tension and facilitate new service deployment, we attempt to apply network virtualization in FiWi networks, in which the network＇s control plane and data plane are separated from each other. Based on a previously proposed hierarchical model and service model for FiWi network virtualization, the process of service implementation is described. The performances of the FiWi access networks applying network virtualization are analyzed in detail, including bandwidth for links, throughput for nodes, and multipath flow transmission. Simulation results show that the FiWi network with virtualization is superior to that without.     

基于Citrix XenApp的高校校园网应用虚拟化设计及研究

针对高校校园网络环境下传统计算机应用软件使用和管理中存在的问题,分析了虚拟化及应用虚拟化技术的原理,比较了当前较为成熟的各种技术,设计出基于Citrix XenApp的高校校园网应用虚拟化系统解决方案,重点解决了校园网中软件安装、软件兼容性、软件升级、软件安全性等问题,并研究了应用虚拟化技术优势及其使用过程中应注意的硬件兼容性、应用软件授权等问题。应用虚拟化可以较好地解决目前校园网中软件管理及使用中所面临的大多数问题。     

网络功能虚拟化技术综述

无论是企业网还是数据中心,为实现相关应用功能、提升网络性能和加强网络安全等部署了大量的网络功能设备,但这些网络功能设备大多基于硬件,存在功能固化、扩展能力差、统一管理困难等问题。为解决上述问题,学术界和工业界不约而同将目光投向了网络功能虚拟化NFV技术。通过解耦网络功能和物理设备,使网络功能不受物理设备的约束,便于网络设备服务的升级更新,同时,NFV为新的体系结构、系统和应用的产生提供了可能。首先介绍了NFV技术,并与云计算和SDN进行对比,然后从VNF的系统结构、数据平面、控制平面、部署方式、实现语言和应用6个维度详细阐述了当前的研究成果,最后总结并展望了NFV未来的研究发展方向。     

基于软件定义网络的可靠性虚拟网络映射保障机制

在软件定义网络（SDN）虚拟网络映射中,现有研究者主要考虑请求接受率方面,而忽视了SDN中底层资源失效的问题。为此,针对SDN中可靠性虚拟网络映射（SVNE）问题,提出了一种联合先验式保护和后验式恢复的虚拟网络映射保障机制。首先,在虚拟请求接受之前,对SDN物理网络区域性资源进行感知;然后,采用先验式保护机制为映射域内相对剩余资源变小的虚拟网络元素预留备份物理资源,并将此扩展虚拟网络通过D-ViNE算法映射至物理网络中;最后,在未备份虚拟网络元素发生故障时,采用后验式恢复算法完成故障的恢复,对节点和链路分别采用重映射和重路由的方法完成恢复。实验结果表明,与基于SDN的生存性虚拟网络映射算法（SDN-SVNE）相比,在虚拟请求接受率方面提高了21.9%。另外,该保护机制在虚拟级别故障恢复率、物理级别故障恢复率等方面也具有优势。     

云桌面虚拟化技术环境下的高职学生多元化网络虚拟技术教育研究

基于云桌面网络虚拟化技术互动网络创新模式,研究了利用云桌面虚拟化创新性来开展高职学生多元化网络虚拟技术教育的工作模式。同时,分析了传统单一化网络虚拟技术教育的局限性,探讨了创建云桌面网络虚拟化技术互动网络创新模式教育的热点话题,提出了基于云桌面虚拟化技术环境下多元化网络虚拟技术教育在云桌面网络虚拟化技术互动下的创新决策。     

基于网络收缩的节点可复用虚拟网络映射算法

针对节点可复用虚拟网络映射中随机节点复用不能较好地利用节点可复用特点的问题,提出了一种基于网络收缩的节点可复用虚拟网络映射算法。通过将网络映射分为网络收缩与映射阶段,将复用节点选择与映射过程分离。在网络收缩过程中,针对收缩网络特性提出了基于邻居节点合并的网络收缩算法,该算法能够在约束最大节点资源需求与最大链路资源需求的条件下,取得较小的网络规模。实验证明,基于网络收缩的节点可复用虚拟网络映射算法具有更优的映射质量以及更少的时间消耗。     

Covering a line segment with variable radius discs

The paper addresses the problem of locating sensors with a circular field of view so that a given line segment is under full surveillance, which is termed as the disc covering problem on a line. The cost of each sensor includes a fixed component f, and a variable component that is a convex function of the diameter of the field-of-view area. When only one type of sensor or, in general, one type of disc, is available, then a simple polynomial algorithm solves the problem. When there are different types of sensors, the problem becomes hard. A branch-and-bound algorithm as well as an efficient heuristic are developed for the special case in which the variable cost component of each sensor is proportional to the square of the measure of the field-of-view area. The heuristic very often obtains the optimal solution as shown in extensive computational testing.     

Integrated strategic and tactical planning in a supply chain network design with a heuristic solution method

In the current competitive business world, viable companies are those that have flexible strategies and long-term plans, by which they can appropriately respond to a dynamic environment. These strategies are used to find the optimum allocation of company income to the main sources of development, for the expansion of company activities and for service expansions.     

An integer encoding grey wolf optimizer for virtual network function placement

This paper studies the virtual network function placement (VNF-P) problem in the context of network function virtualization (NFV), where the end-to-end delay of a requested service function chain (SFC) is minimized and the compute, storage, I/O and bandwidth resources are considered. To address this problem, an integer encoding grey wolf optimizer (IEGWO) is proposed. IEGWO has two significant features, namely an integer encoding scheme and a new wolf position update mechanism. The integer encoding scheme is problem-specific and offers a natural way to represent VNF-P solutions. The proposed wolf position update mechanism divides the wolf pack into two groups in each iteration, where one group performs exploitation while the other focuses on global exploration. It provides the search with a balanced local exploitation and global exploration during evolution. Performance evaluation has been conducted based on 20 test instances and IEGWO is compared with five state-of-the-art meta-heuristics, including the black hole algorithm (BH), the genetic algorithm (GA), the group counseling optimization (GCO), the particle swarm optimization (PSO) and the teaching–learning-based optimization (TLBO). Simulation results demonstrate that compared with BH, GA, GCO, PSO and TLBO, IEGWO achieves significantly better solution quality regarding the mean (standard deviation), boxplot and t-test results of the best fitness values obtained.     

虚拟化与数字仿真融合的多尺度网络复现技术

网络复现技术是面向网络安全实验的效果评估平台与网络仿真系统的基石。针对网络复现的逼真性与可伸缩性需求,提出一种基于云平台的轻量级虚拟化、全虚拟化与数字仿真三种尺度融合的多尺度网络复现技术。首先介绍了面向三种尺度无缝融合的网络复现体系架构,然后重点研究了基于该体系架构的网络构建技术。仿真实验结果表明,三种尺度无缝融合的网络构建技术所复现的仿真网络具有灵活、透明、并发的特点;此外,通过该技术所复现的仿真网络还具有高度可扩展性;最后,在所复现的大规模仿真网络上进行多种协议通信测试与简单网络安全实验,验证了该大规模仿真网络的实用性。实验验证结果表明,虚拟化与数字仿真融合的多尺度网络复现技术可作为创建大规模仿真网络的技术支撑。     

Scheduling network maintenance jobs with release dates and deadlines to maximize total flow over time: Bounds and solution strategies

We consider a problem that marries network flows and scheduling, motivated by the need to schedule maintenance activities in infrastructure networks, such as rail or general logistics networks. Network elements must undergo regular preventive maintenance, shutting down the arc for the duration of the activity. Careful coordination of these arc maintenance jobs can dramatically reduce the impact of such shutdown jobs on the flow carried by the network. Scheduling such jobs between given release dates and deadlines so as to maximize the total flow over time presents an intriguing case to study the role of time discretization. Here we prove that if the problem data is integer, and no flow can be stored at nodes, we can restrict attention to integer job start times. However if flow can be stored, fractional start times may be needed. This makes traditional strong integer programming scheduling models difficult to apply. Here we formulate an exact integer programming model for the continuous time problem, as well as integer programming models based on time discretization that can provide dual bounds, and that can – with minor modifications – also yield primal bounds. The resulting bounds are demonstrated to have small gaps on test instances, and offer a good trade-off for bound quality against computing time.     

基于异质信息网络的安卓虚拟化程序检测方法

考虑到安卓应用虚拟化技术的功能特性,精确检测安卓虚拟化程序是识别其隐藏安全风险的基础和必要前提。为此,提出了基于异质信息网络的安卓虚拟化程序检测方法,并实现了原型系统Aiplugin。根据安卓虚拟化程序的特点,提取四类静态程序特征,并将程序特征映射到异质信息网络上,以元路径的形式将不同程序关联起来。采用异质图注意力网络表征算法和OC-SVM算法,融合不同视图的程序语义信息,实现对安卓虚拟化程序的表征和分类。实验结果表明,相较于当前的代表性工具VAhunt, Aiplugin可有效检测包括平行空间等更多类型的安卓虚拟化程序。