基于SFC的业务路径选择算法

产业界正在推进SFC(service function chain,业务链)技术发展,以实现云网融合的新型业务部署模式.业务路径选择策略是业务链的核心技术,直接影响网络性能与业务体验.针对现有业务路径选择策略存在的单一目标缺陷,创新提出并仿真验证了一种基于时延和负载的最小权重法,优化网络资源分配,提升业务链处理性能,为运营商未来网络和资源部署提供技术参考.     

NFV故障关联及故障自愈方案研究

利用NFV实现电信网络设备软硬件解耦,摆脱了对专用硬件设备的依赖,并能利用NFV资源共享、自动部署、弹性伸缩等特性,但同时也带来了故障点多、故障定位分析困难等问题,导致NFV网络的运营维护难度增加。分析了NFV故障处理的流程及故障关联方案,为NFV场景下的故障定位处理提供指引。同时给出了VNF故障自愈实现方案,利用VNF特性,实现网元及业务自愈,提升自动化运维能力。     

运营商NFV引入关键问题分析

电信网引入NFV可以实现资源灵活共享,业务快速开发和上线,使得CT网络具备了IT的灵活性.NFV是一项综合性技术,虚拟化、可靠性、QoS、安全和云平台等技术是NFV能否成功落地的关键.介绍运营商面临的问题和诉求,然后具体说明核心网适合优先引入NFV,并对NFV解决方案关键问题进行分析.     

基于SDN和NFV的业务链管理系统研究

首先针对目前业务链系统拓扑固化、扩展难的问题,分析业务链系统SDN化以及NFV化的需求。SDN的基本特征是转发与控制分离,集中的软件控制以及开放的编程接口,通过使用SDN技术,可以使得业务链系统具有灵活、拓扑可视、扩展方便等特性。然后,分析基于SDN技术实现业务链管理系统的技术架构,基于SDN的业务链系统主要由业务链管理系统、策略管理系统、SDN控制器、流分类器、SDN交换机等主要组件组成。文章对其中的关键技术进行了研究分析,最后,对业务链应用场景进行了研究,目前业务链系统在数据中心、Gi-LAN、接入网等场景有大量的应用需求。     

基于SDN和NFV的5G移动通信网络架构分析

软件定义网络以及网络功能虚拟化是当前业界研究的重点,为了实现移动通信网络架构的优化,需要对当前网络中存在的问题进行总结,充分发挥出SDN、NFV的技术优势,基于SDN、NFV理论对移动网络架构进行深入研究,明确后期的设计思路.本文首先对5G移动通信技术进行分析,其次基于SDN和NFV建立5G移动通信网络架构,然后提出该架构功能的具体实现路径,供相关人士参考.     

业务平台云化集中部署方案研究

随着NFV技术在通信行业的广泛应用,越来越多的运营商采用NFV架构的业务平台集中部署在云资源池,实现集中化运维从而达到降低维护成本的目的。本文以统一Centrex业务平台集中化为例,重点研究了业务平台集中化部署过程中的关键问题,并对该类业务平台集中化建设多种方案进行了详细的分析和比较     

基于SDN和NFV的云数据中心网络服务

虚拟私有云和业务链是云数据中心下一阶段提供的核心网络服务,它们对现有云数据中心网络造成了巨大挑战,如网络虚拟化、业务编排、网络并发配置能力等,SDN和NFV是解决以上问题的技术手段。本文对SDN和NFV的解决方案、产业链成熟度以及未来研究的重点进行了分析和研究。     

基于重叠网络结构的服务功能链时空优化编排策略

网络功能虚拟化(NFV)的引入大幅降低了互联网业务的运营成本。针对现有的服务功能链(SFC)编排方法无法在优化底层资源的同时保证业务时延性能的问题,该文提出一种基于重叠网络结构的SFC时空优化编排策略。在将计算、网络资源与细粒度时延约束纳入考虑的基础上,该策略通过建立重叠网络模型实现了计算与网络资源的分离,将构建SFC所需的资源开销与相关时延共同抽象化为重叠网络链路权重,从而使SFC编排问题转化为易于求解的最短路径问题。对于需要批量处理的SFC集合设计了基于重叠网络的模拟退火迭代优化编排算法(ONSA)。通过对比实验证明了该策略下编排方案的平均端到端时延、链路资源占用率与运营开销相对其他方案分别降低29.5%, 12.4%与15.2%,请求接受率提高22.3%,虚拟网络功能(VNF)负载均衡性能得到显著提升。     

效用及实时业务QoS联合保证的资源分配方案

为了有效利用和公平分配有限的网络资源,从而优化网络性能,提高社会福利,该文提出了一种效用及实时业务QoS联合保证的资源分配方案,将效用模型与具有不同服务质量需求的业务相结合,引入需求带宽和期望带宽,建立优化模型.方案不仅体现了资源分配效率的要求,达到系统效用的最优,同时优先保证实时业务的QoS要求,并达到QoS保证业务和尽力而为(Best Effort, BE)业务之间的公平分配.仿真结果表明,提出的方案较传统方案有效地提高了系统总效用,实现了面向服务的资源分配目标,优化了网络系统的整体性能.     

vBRAS与VxLAN黄金组合:吹响运营商向新型城域网变革的号角

2016年初,经过苛刻测试和验证,新华三基于SDN&NFV的vBRAS资源池解决方案成功部署浙江电信城域网,这是中国地区运营商第一次大范围NFV城域网商用实践,开辟了运营商NFV技术应用先河. 随着互联网新业务的部署和发展,传统的宽带城域网边缘设备(BRAS)呈现出业务能力不足、新增业务功能开发周期长等问题.不过NFV技术的发展,给城域网建设注入了新元素,运营商开始通过虚拟BRAS设备(vBRAS)对城域网进行改造,实现业务自动灵活部署、多业务融合管理.     

基于联合备份的服务功能链可靠性保障的部署方法

在网络功能虚拟化(NFV)环境中,针对服务功能链(SFC)部署时的可靠性问题,该文提出对备份虚拟网络功能选择、备份实例放置和服务功能链部署的联合优化方法。首先,定义一个单位开销可靠性提高值的虚拟网络功能衡量标准,改进备份虚拟网络功能选择方法;其次,采用联合备份的方式调整相邻备份实例之间的放置策略,以降低带宽资源开销;最后,将整个服务功能链可靠性保障的部署问题构建成整数线性规划模型,并提出一种基于最短路径的启发式算法,克服整数线性规划求解的复杂性。仿真结果表明,该方法在优先满足网络服务可靠性需求的同时,优化资源配置,提高了请求接受率。     

运营商网络中面向时延优化的服务功能链迁移重配置策略

在引入网络功能虚拟化（NFV）后,运营商能基于通用的计算和网络平台提供更具弹性的服务.本文研究基于NFV的运营商网络中的服务功能链（SFC）的迁移重配置问题.首先分析了业务流的动态性导致的运营商网络中资源利用率下降和负载不均的问题,然后在将计算资源、网络资源和业务流端到端限制等条件纳入考虑的基础上,建立了面向业务流服务时延优化的数学模型,并证明了SFC迁移重配置问题的NP性.进一步基于遗传算法提出了一种易于部署的启发式SFC迁移重配置策略.通过详细的对比实验表明算法在端到端时延、链路带宽资源占用率和通用服务器资源占用率等关键指标上优于其它方案12.3%、10.9%和15.8%以上.这进一步说明所提出方案通过对运营商网络资源的合理调配,在保证业务流服务质量的同时有效改善了资源利用效率.     

网络功能虚拟化场景下的并行加速

在网络功能虚拟化场景下,每个网络功能都以软件的形式来实现。但传统的串行网络功能组链方式将带来极大的性能损耗。而如今针对网络功能进行加速的工作,主要集中在优化该串行链的每个组成成分上。提出了一个高性能的系统框架,通过让网络功能并行地对数据分组进行处理,提高网络功能虚拟化场景下服务链的整体性能。该系统由 3 个部分组成。首先,该系统为管理员提供了一个策略描述语言来直观地描述串行或并行的组链意图。然后,该系统的编排器智能地鉴别网络功能之间的依赖性,并基于所提供的策略,生成高性能的“服务图”。最后,该系统的底层实现通过执行轻量级的数据分组复制、分布式的并行分组转发和负载均衡后的数据分组合并来支持网络功能的并行处理。基于DPDK技术,在Linux容器中实现了该系统的原型机。通过实验验证可知,该系统能极大地减少真实世界中服务链的处理时延。     

Adaptive routing and wavelength assignment method based on SDN

Routing and wavelength assignment is an important resource allocation method of all-optical network.Aiming at the problem of traditional method combined with the new architecture,an adaptive multi-objective routing and wavelength assignment method based on SDN was proposed,which could realize the allocation of link resources of all-optical network through self-regulation.Based on the SDN service function chain model,service scheduling time and link service quality were taken as the scheduling objective,routing and wavelength assignment problem was constructed as the 0-1 integer programming problem,meanwhile,binary hybrid topology particle swarm optimization algorithm was used to optimize the network resources for optimal scheduling.The simulation results show that the proposed method is superior to the traditional classical algorithms in the test of recovery time,blocking rate and resource utilization.     

Resource optimization algorithm of combination of NFV and SDN for application of multiple services

Various services of internet of things (IoT) require flexible network deployment to guarantee different quality of service (QoS).Aiming at the problem of IoT service function chain deployment,network function virtualization (NFV) and software defined networking (SDN) were combined to optimize resources.Considering forwarding cost and traffic load balance,a joint optimization model of virtual network function placement and service function chain routing was given and was proved to be NP-Hard.In order to solve this model,two heuristic algorithms were proposed.One was the service chain deployment algorithm of first routing then placing (FRTP) and the other was the placing followed by routing (PFBR) based on node priority.Simulation results demonstrate that FRTP and PFBR algorithm can significantly balance network traffic load while alleviating congestion and improving the acceptance ratio of the chain requests compared with other algorithms.     

一种面向安全的虚拟网络功能动态异构调度方法

网络功能虚拟化(NFV)为服务链构建带来了灵活性与动态性,然而,软件化与虚拟化环境可能存在软件漏洞、后门等安全风险,对服务链(SC)的安全产生影响。为此,该文提出一种服务链上虚拟网络功能(VNF)调度方法。首先,为虚拟网络功能构建异构镜像池,避免利用共模漏洞的大范围攻击;随后,以特定周期选择服务链虚拟网络功能进行调度,加载异构镜像对该网络功能的执行实体进行替换;最后,考虑调度对网络功能性能的影响,应用斯坦科尔伯格博弈对攻防过程建模,以最优化防御者收益为目标求解服务链上各网络功能的调度概率。实验表明,该方法能够降低攻击者攻击成功率,同时将调度产生的开销控制在可接受范围内。     

基于深度Q网络的在线服务功能链部署方法

针对5G网络资源状态动态变化和网络模型高维度下服务功能链部署的复杂性问题,该文提出一种基于深度Q网络的在线服务功能链部署方法(DeePSCD)。首先,为描述网络资源动态变化的特征,将服务功能链部署建模成马尔可夫决策过程,然后,针对系统资源模型的高维度问题采用深度Q网络的方法进行在线服务功能链部署策略求解。该方法可以有效描述网络资源状态的动态变化,特别是深度Q网络能有效克服求解复杂度,优化服务功能链的部署开销。仿真结果表明,所提方法在满足服务时延约束条件下降低了服务功能链的部署开销,提高了运营商网络的服务请求接受率。     

Intelligent Service Function Chain Mapping Framework for Cloud-and-Edge-Collaborative IoT

With the rapid development of Internet of thing (IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization ( NFV) technology with cloud computing and mobile edge computing ( MEC), an NFV-enabled cloud-and-edge-collaborative IoT (CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain (SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping (DQLBM) algorithm as well as an energy-based topology adjustment (EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.     

Virtual network function–forwarding graph embedding: A genetic algorithm approach

Network function virtualization (NFV) provides a simple and effective mean to deploy and manage network and telecommunications' services. A typical service can be expressed in the form of a virtual network function–forwarding graph (VNF‐FG). Allocating a VNF‐FG is equivalent to place VNFs and virtual links onto a given substrate network considering resources and quality‐of‐service (QoS) constraints. The deployment of VNF‐FGs in large‐scale networks, such that QoS measures and deployment cost are optimized, is an emerging challenge. Single‐objective VNF‐FGs allocation has been addressed in existing literature; however, there is still a lack of studies considering multiobjective VNF‐FGs allocation. In addition, it is not trivial to obtain optimal VNF‐FGs allocation due to its high computational complexity even in case of single‐objective VNF‐FGs allocation. Genetic algorithms (GAs) have been proved its ability in coping with multiobjective optimization problems; thus, we propose a GA‐based scheme to solve multiobjective VNF‐FGs allocation problem in this paper. The numerical results confirm that the proposed scheme can provide near Pareto‐optimal solutions within a short execution time.     

The intelligent multi‐domain service function chain deployment: Architecture,challenges and solutions

Network function virtualization (NFV) technology achieves flexible service deployment by replacing the middleboxes with virtual network functions (VNFs). In NFV, a set of VNFs are chained in a given order, called service function chain (SFC), and accordingly, data flow is steered to traverse all the VNFs in order to offer a service. With a large number of network devices and end users being connected into Internet, there is a growing demand for large‐scale multi‐domain networks to dynamically deploy the SFC across multiple network domains, in order to support efficient service provisioning. To this end, in this paper, we first investigate the state of the art of multi‐domain SFC deployment, and then propose an intelligent multi‐domain SFC deployment (IMSD) architecture by leveraging software‐defined networking (SDN), NFV, and deep learning technologies. Furthermore, we discuss the potential challenges to realize the IMSD and provide some promising solutions.