Automatic data aggregation for recursively modeled NFV services

Network function virtualization (NFV) allows to model network services as graphs interconnecting virtual network functions (VNFs), which may include nested VNFs, modeled as subgraphs of VNFs or end points. To query the performance data of network services modeled as abstract and high‐level graphs is challenging because of the recursivity of the NFV architecture and the elasticity and dynamicity provided by the NFV infrastructure. We propose to use Datalog, a declarative logic programming language, to build a framework that supports efficient data aggregation for performance metrics of recursively modeled network services. We present our recursive query language for automatic and flexible decomposition and aggregation of NFV performance metrics and describe example use cases for both compute and network metrics. We also describe the design and implementation of a proof‐of‐concept query engine using the language. Our performance evaluation shows that the total query latency is dominated by retrieval times of performance metrics from infrastructure databases, and the time for execution and automatic decomposition of high‐level queries by the query engine itself increases linearly with the size of the service graph up to 1000 nodes. Hence, our evaluations show that the query engine scales well, bounded mainly by the limited execution capabilities of our test environment. Furthermore, it can handle multiple concurrent queries up to the concurrency limits of the backend database in use. The proposed query language and engine are thus effective in recursively retrieving performance metrics of NFV environments supporting large‐scale service graphs and large numbers of query requests.     

Joint service function chain deploying and path selection for bandwidth saving and VNF reuse

Service function chain (SFC) is managed as a set of chained virtual network functions (VNFs) in a particular order. The efficiency of a network closely depends on how these VNFs are deployed in the network. In this paper, we study the joint problem of service function chain deploying and path selection for bandwidth saving and VNF reuse. We first describes the problem as a multiobjective and multirestriction problem, and then a heuristic service function chain deployment algorithm based on longest function assignment sequence is proposed. Via simulations, we show that our design can reduce the number of deployed VNFs and link bandwidth requirement jointly and serve more requests than other algorithms.     

A low latency service function chain with SR-I/OV in software defined networks

A network flow is required to be processed by multiple network functions such as PGW and SGW in mobile networks as a service function chain (SFC). Compared to hardware-based network functions, virtualized network functions are more flexible for deployment. Software defined network (SDN) provides a centralized network architecture to manage network resources and route the network flow among network functions in sequence and virtual machines are leveraged to deploy the network functions as network function virtualization (NFV). However, currently the performance of NFV suffers from I/O latency because packet processing causes lots of interrupts that decreases CPU utilization. To address the I/O latency issue, SR-I/OV network card is designed to replace OpenvSwitch in host machines to reduce the system interrupts. However, SR-I/OV is not compatible with existing SDN system, which is an important component in future 5G networks. Therefore, we propose an integrated architecture called the low latency service function chain from a wider perspective in system design to overcome main defects described above. We modify appropriate components in SR-I/OV driver and OpenvSwitch to dramatically reduce packet processing latency in SFC composed by several VNFs. Moreover, our design is compatible with SDN environment and benefited by central control.

     

基于流量演化感知的服务功能链在线弹性编排策略

随着网络功能虚拟化（NFV）的引入，运营商能够提供更为灵活的网络服务.然而现有服务功能链（SFC）编排局限于静态反应式策略，业务流量发生变化时网络资源供应量难以匹配负载需求，虚拟网络功能（VNF）频繁部署与迁移，运营开销增大.针对上述问题，该文提出一种基于流量演化感知的服务功能链在线弹性编排策略（OEOP），该策略将在线学习引入到SFC流量演化感知的过程，预先确定细粒度的VNF弹性需求.此外，以实时更新的SFC路径与节点负载两因子为导向，完成新增VNF的在线弹性部署，代替VNF迁移应对系统负载变化.仿真表明，该策略明显增强了虚拟资源供应量与负载需求的匹配特性，VNF吞吐量利用率提高10.2%~24.8%，运营开销平均降低26.7%.     

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.     

一种面向运营成本优化的虚拟网络功能部署和路由分配策略

随着网络功能虚拟化(NFV)技术的发展,虚拟网络功能(VNF)可以通过服务功能链(SFC)的形式部署在如虚拟机的通用平台中,为管理带来灵活性。但是对于服务提供商来说,由于网络基础设施的复杂性和日益增长的服务需求,给VNF的部署带来了高昂的运营成本(OPEX)。针对此问题,该文提出一种面向OPEX优化的策略,旨在最小化OPEX中的激活、能耗和传输成本,得到VNF部署和路由分配优化方案。为此建立一种全新的混合整数线性规划(MILP)模型,并设计包括遗传算法(GA)在内的3种OPEX优化算法。仿真实验评估在不同资源配给下MILP和3种算法的OPEX及其性能,其中GA算法在节点资源配比60%以上时可以得到近似于MILP模型的解决方案。     

Intent‐based service management for heterogeneous software‐defined infrastructure domains

One of the main challenges in delivering end‐to‐end service chains across multiple software‐defined networking (SDN) and network function virtualization (NFV) domains is to achieve unified management and orchestration functions. A very critical aspect is the definition of an open, vendor‐agnostic, and interoperable northbound interface (NBI) that should be as abstract as possible and decoupled from domain‐specific data and control plane technologies. In this paper, we propose a reference architecture and an intent‐based NBI for end‐to‐end service management across multiple technological domains. The general approach is tested in a heterogeneous OpenFlow/Internet‐of‐Things (IoT) SDN test bed, where the proposed solution is applied to a rather complex service provisioning scenario spanning three different technological domains: an IoT infrastructure deployment, a cloud‐based data collection, processing, and publishing platform, and a transport domain over a geographic network interconnecting the IoT domain and the data center hosting the cloud services.     

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.     

Separating VNF and Network Control for Hardware‐Acceleration of SDN/NFV Architecture

A hardware‐acceleration architecture that separates virtual network functions (VNFs) and network control (called HSN) is proposed to solve the mismatch between the simple flow steering requirements and strong packet processing abilities of software‐defined networking (SDN) forwarding elements (FEs) in SDN/network function virtualization (NFV) architecture, while improving the efficiency of NFV infrastructure and the performance of network‐intensive functions. HSN makes full use of FEs and accelerates VNFs through two mechanisms: (1) separation of traffic steering and packet processing in the FEs; (2) separation of SDN and NFV control in the FEs. Our HSN prototype, built on NetFPGA‐10G, demonstrates that the processing performance can be greatly improved with only a small modification of the traditional SDN/NFV architecture.     

基于深度强化学习的服务功能链可靠部署算法

针对当前关于服务功能链(SFC)的部署问题都未考虑到虚拟网络功能(VNF)的失效重要度,该文提出了基于深度强化学习的SFC可靠部署算法。首先建立VNF和虚拟链路可靠映射模型,为重要的VNF设置高可靠性需求,并通过链路部署长度限制尽可能保证虚拟链路可靠性需求。其次,以负载均衡为资源协调原则,与VNF可靠性联合优化,最终使用深度强化学习得到服务功能链部署策略。另外,提出了基于重要度的节点备份和链路备份策略,用于应对部署过程中VNF/链路可靠性难以满足的情况。仿真结果表明,该文的可靠部署算法在保证可靠性需求的基础上能够有效减少SFC失效损失,同时使虚拟网络更加稳定可靠。     

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。     

5G网络切片场景中基于预测的虚拟网络功能动态部署算法

针对无线虚拟化网络在时间域上业务请求的动态变化和信息反馈时延导致虚拟资源分配的不合理,该文提出一种基于长短时记忆(LSTM)网络的流量感知算法,该算法通过服务功能链(SFC)的历史队列信息来预测未来负载状态。基于预测的结果,联合考虑虚拟网络功能(VNF)的调度问题和相应的计算资源分配问题,提出一种基于最大最小蚁群算法(MMACA)的虚拟网络功能动态部署方法,在满足未来队列不溢出的最低资源需求的前提下,采用按需分配的方式最大化计算资源利用率。仿真结果表明,该文提出的基于LSTM神经网络预测模型能够获得很好的预测效果,实现了网络的在线监测;基于MMACA的VNF部署方法有效降低了比特丢失率的同时也降低了整体VNF调度产生的平均端到端时延。     

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.     

解决NFV环境下拨号上网地址占用问题的NAT444方案

虚拟化技术的兴起,打破了网络设备的传统形态。电信运营商期望在通用硬件架构上部署NFV,以降低CAPEX和OPEX成本,提升运营效率,实现快速提供新服务的能力,提升运营商收入。但是,NFV虚拟化技术有其独有特点,若在政企宽带拨号上网类业务中使用与传统网络设备相同的部署和配置方式,会引发公网IPv4地址的大量消耗,提出在NFV环境中引入NAT444作为解决方案,并对部署、溯源方式进行了研究。     

Joint server and network optimization toward load‐balanced service chaining

Prior service chaining systems address server and network load balancing issues individually, which may not ensure efficient system performance. To this end, in this paper, we study the design, implementation, complexity analysis, and evaluation of Nearest First and Local‐Global Transformation algorithm, which jointly supports server and network load balancing for chaining virtualized network functions (VNFs) in data center environment. The algorithm firstly constructs service chains by a greedy strategy, which both considers server and network latency. Then a searching technique, which replaces a selected VNF with another candidate and swaps the order of VNFs in service chains, is applied to improve the solutions. We have implemented the algorithm using Software‐defined networking and OpenFlow concepts. The numerical results indicate that, compared with a sequential approach, Nearest First and Local‐Global Transformation increases the system bandwidth utilization up to 45%. The results also show that it is worth applying the second phase of our algorithm since it considerably enhances the system performance by 20%.     

RNN-EdgeQL: An auto-scaling and placement approach for SFC

This paper proposes a prediction-based scaling and placement of service function chains (SFCs) to improve service level agreement (SLA) and reduce operation cost. We used a variant of recurrent neural network (RNN) called gated recurrent unit (GRU) for resource demand prediction. Then, considering these predictions, we built an intuitive scale in/out algorithm. We also developed an algorithm that applies Q-Learning on Edge computing environment (EdgeQL) to place these scaled-out VNFs in appropriate locations. The integrated algorithm that combines prediction, scaling, and placement are called RNN-EdgeQL. RNN-EdgeQL (v2) is further improved to achieve application agnostic group level elasticity in the chain, independent of applications installed on the VNFs. We tested our algorithm on two realistic temporal dynamic load models including Internet traffic (Abilene) and an application specific traffic (Wiki) on an OpenStack testbed. The contribution of this article is threefold. First, prediction model prepares the target SFC for the upcoming load. Second, an application agnostic characteristics of the algorithm achieves the group-level elasticity in SFC. Finally, the EdgeQL placement model minimizes the end-to-end path of an SFC in multi-access edge computing (MEC) environment. As a result, RNN-EdgeQL (v2) gives the lowest overall latency, lowest SLA violations, and lowest VNFs requirement, compared to RNN-EdgeQL (v1) and Threshold-Openstack default placement.     

移动边缘网络中基于用户移动的服务功能链迁移策略

移动边缘计算(Mobile Edge Computing,MEC)通过在网络边缘部署服务器,提供计算和存储资源,可为用户提供超低时延和高带宽业务。网络功能虚拟化(Network Function Virtualization,NFV)与MEC技术相结合,可在MEC服务器上提供服务功能链(Service Function Chain,SFC),提升用户的业务体验。为了保证移动用户的服务质量,需要在用户跨基站移动时将SFC迁移到合适的边缘服务器上。主要以最小化用户服务的端到端时延和运行成本为目标,提出了MEC网络中具有资源容量约束的SFC迁移策略,以实现移动用户业务的无缝迁移。仿真结果表明,与现有方案相比,该策略具有更好的有效性和高效性。     

运营商网络中面向资源碎片优化的网络服务链构建策略

网络功能虚拟化(NFV)的引入使得运营商网络的网络功能不再依赖于专用硬件设备且服务能力更具弹性。为更有效地使用基于NFV的运营商网络中的资源,该文提出一种网络服务链(NSC)的优化构建策略。和已有研究工作不同,该文从减少因计算资源和网络资源这两类资源的不合理使用产生的资源碎片角度出发,以最大化运营商网络能容纳的业务流数量为目标,建立NSC构建的数学模型,且提出一种新的贪心NSC构建策略,该策略融合了路径选择和NSC的多个VNF部署。数值仿真实验表明,在相同资源总量的情况下与典型策略相比,所提策略能够容纳更多的业务流,实现更低的端到端的数据延迟,有效提高了运营商网络中通用服务器和交换机的资源利用率。     

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.     

移动边缘计算中服务功能链的自适应优化部署策略

为解决移动边缘计算中面向用户的服务功能链（Service Function Chain,SFC）部署成本开销过大、时延过长问题,提出了针对SFC的支出成本与时延联合自适应优化的部署策略。首先,在虚拟网络功能（Virtualized Network Function,VNF）节点选取阶段,考虑路径损耗这一无线信道衰落问题,根据有线用户与无线用户的位置情况,选择当前最佳节点以降低SFC的响应时延。其次,在服务节点配置阶段,根据用户请求处理的数据内容的新鲜度记录,自适应动态增加和删减相应的缓存,利用资源感知算法在保证数据传递可靠性的同时,减少服务节点的配置个数,降低配置开销。最后,在SFC部署阶段,利用基于KSP（K-shortest Paths）的功耗感知算法确定最佳节点映射排序与通信链路,在减少通信链路重映射的同时还能保证部署的SFC的低成本与低时延。实验仿真结果表明,相比于已有方案,该方法能够有效降低部署成本与时延,并能对不同用户的SFC部署做到自适应优化,提高了SFC的部署成功率。