面向下一代网络的端到端多路径传输层架构

为了解决传统网络无法有效同时使用多家乡终端的多个接口传输数据的问题,提出了一种面向下一代网络的端到端多路径传输层架构-E2EMP.E2EMP通过自适应的根据路径特性分发数据,采用双层序列空间,实施灵活的端到端路径管理,提高了多家乡终端的传输性能.实验仿真表明,E2EMP能够有效地聚合终端多家乡的出口带宽,同时提高了数据传输的安全性和可靠性.     

端到端确定性网络架构和关键技术

新型业务如远程控制、虚拟/增强现实等对网络提出了更高的带宽和时延要求,传统网络已经难以满足。面向6G的网络演进,需要端到端确定性网络技术保障。针对当前技术标准研究缺乏对于端到端时延保障的全局考虑,指出了确定性网络需要同时解决单跳和全局的时延问题,提出了端到端确定性网络架构,以及跨网络域和跨协议层的确定性网络技术方案。最后,针对当前确定性网络应用部署的难点,提出了相应的推进建议。     

QoS路由的多路径算法

本文分析了预约网络中采用多路径路由传送数据以保证应用的带宽要求、和/或端到端时延限制条件.文中提出的路由方案综合了单路径和多路径路由的优点:通过多路径路由以降低呼叫阻塞率,同时侧重单路径路由以降低协议开销.在考察网络中的四个一般性问题基础上,提出了启发式算法.     

面向路径的无线多跳网络端-端吞吐量分析

基于802.11 DCF机制的无线多跳网络性能深受MAC层的介质访问机制和上层路由机制相互作用的影响.本文面向自组织网络路径,给出了端-端最优吞吐量的模型以及计算其上下界的方法.本文的研究更加注重无线多跳网络的实际特性:分组调度可以任意方式调度,节点的载波侦听范围大于其传输范围.研究发现路径的端到端吞吐量与路径长度、分组发送速率和分组调度策略等因素密切相关.本文还分析了其他因素对端-端带宽的影响程度.本文从面向路径的分析模拟工作中得出的一些独特结论相信将有助于上层应用程序以及路由协议的研究.     

网络端端可靠度的上下界计算研究

考虑精确计算较大规模网络的端端可靠度属于NP—hard问题,提出一种计算网络端端可靠度的近似方法,算法分别基于最小路集和不交最短路集来计算网络端端可靠度的上下界,并在此基础上给出了示例来阐明算法的有效性,该计算方法的算法实现简单,能快速计算出网络的端端相对可靠度。     

实时业务端到端QoS测评

业务对网络性能的要求直接来源于业务对端到端客户感知(主观质量)的满意程度的需求.尤其对于目前在网络中涌现出的越来越多的实时业务,如VoIP、IPTV、 VOD 等业务对端到端的可用带宽、时延、抖动和丢包十分敏感,这些性能参数严重影响着此类业务的开展和运营维护.这些指标的组合整体反映了承载网络所能提供的QoS,因此准确把握这些网络QoS指标是对网络承载性能进行测评的基础.     

端到端家庭宽带网络建设策略

伴随国家政策环境的利好,互联网视频业务迎来宝贵的发展机遇期,大视频时代即将到来,而这也将带动通信行业新一轮的发展。为了能够更好的满足家庭客户带宽需求,加快家宽基础网络建设,提升网络承载能力,提高用户粘性,必须做好家宽网络的规划建设。本文通过对家庭宽带网络端到端的分析,明晰了用户终端、出口带宽、内容网络等关键环节的问题,并提出了相应的网络建设策略。     

中国电信携手华为部署首个端到端100G波分系统

6月22日,华为和中国电信联合宣布,在江苏省骨干网络上成功部署端到端100G业务,为长三角地区两大重要城市南京和无锡之间建立超大带宽的传送链接。100G波长稳定承载于江苏省干OTN网络上,实现省内干线OTN网络端到端100G业务的拉通。极大地提升了网络的整体容量。     

端到端网络带宽测量技术研究

本文对网络带宽相关测度进行了深入研究,重点分析了端到端带宽测度的定义及测量方法,并提出了能概括当前网络带宽测量技术的三种不同模型,在剖析了这些模型的测量原理和优缺点之后,介绍了一些可从网络上获得的典型端到端带宽测量工具及性能评价指标,随后探讨了带宽测量技术将来可能的研究方向以及端到端带宽测量的应用价值.     

端到端的QoS服务保障分析

随着行业竞争的日益激烈,用户感知很大程度上影响了一个业务的发展。因此,用户体验在电信行业越来越重要,也受到各方的关注。随着网络演进,QoS服务将为OTT提供一定的带宽保障,从而带来新的合作模式和更好的用户体验。分析了WCDMA/LTE/PCC网络架构下的端到端QoS带宽保障机制及在业务开展中的相关问题。     

一种基于聚合流的端到端统计时延界的新算法

为分析融合网络中聚合业务的端到端时延性能,提出一种基于聚合流的融合网络端到端统计时延界的新算法.该算法利用MGF(矩母函数)重新表征了网络端到端时延界的MGF形式的概率模型.数值分析结果表明了该算法的有效性和优越性,该算法很大程度上提高了独立统计复用,对融合网络性能评价具有参考意义.     

网络中心战中端到端网络时延的确定

介绍了网络中心战的概念,分析了以太网网络资源参数。在不考虑传输质量和传输成本的前提下,网络时延与其他网络资源参数相关,为其他网络资源参数的函数。它是网络资源的关键因素。提出了一种计算网络中心战中端到端网络时延和获取链路信息的算法,开发了基于Server/Client架构的计算程序。以3节点旅(团)级网络中心战架构为例,计算了端到端网络时延并且获取了对应的链路信息。解决了网络中心战中端到端网络时延确定的关键问题,并且可用于网络中心战中服务质量选路的研究。     

An End-to-End Bandwidth Allocation Algorithm for Ad Hoc Networks

Bandwidth-guaranteed QoS service in ad hoc networks is a challenging task due to several factors such as the absence of the central control, the dynamic network topology, the hidden terminal problem and the multihop routing property. An end-to-end bandwidth allocation algorithm was proposed in [Lin and Liu, 15] to support the QoS service in ad hoc networks. However, without exploring the global resource information along the route, the performance of that algorithm is quite limited. In addition, it also incurs significant control overhead. We develop a new algorithm for end-to-end bandwidth calculation and assignment in ad hoc networks which utilizes the global resource information along the route to determine the available end-to-end bandwidth. Our method also employs the topology-transparent scheduling technology to reduce the control overhead. The proposed algorithm can be efficiently utilized in a distributed manner. Both theoretical analysis and simulation results show that our end-to-end bandwidth allocation scheme can significantly improve the network capacity compared with the existing method.     

How to reconstruct end-to-end traffic based on time-frequency analysis and artificial neural network

End-to-end traffic, which describes the inherent characteristics and end-to-end behaviors of communication networks, is the crucial input parameter of network management and network traffic engineering. This paper proposes a new reconstruction algorithm to develop the research on reconstruction of end-to-end traffic in large-scale communication networks. We firstly conduct the time-frequency analysis on end-to-end traffic, and then localize its features to gain its time-frequency properties before decomposing it into the low-frequency and high-frequency components. We find that if decomposing appropriately, the low-frequency component of end-to-end traffic can accurately reflect its change trend, while its high-frequency component can well show the burst and fluctuation nature. This motivates us to find a reasonable time-frequency decomposition strategy to extract the low-frequency and high-frequency components of end-to-end traffic. Moreover, this further inspires us to use the regressive model to model the low-frequency part, exploit artificial neural network to characterize the high-frequency component, and then combine these two parts according to the regressive model and artificial neural network to precisely reconstruct end-to-end traffic. Simulation results show that in contrast to previous methods our algorithm is much more effective and promising.     

面向多业务场景的端到端网络切片安全部署算法

5G移动通信中,网络切片(NS)的引入成功解决了不同业务场景的网络资源分配不均问题。针对传统算法无法满足5G网络的多业务场景切片安全部署问题,该文提出一种针对于多业务场景的端到端网络切片安全需求(NSR)部署算法。首先,针对切片部署过程中节点的安全性进行了定义;其次,根据节点的安全性进行排序和映射,在此基础上,以最小化网络资源部署成本的同时提高部署的安全收益为目标,构建切片部署的数学模型;最后,考虑到每种类型切片的资源需求不同,提出一种针对性的部署算法实现端到端网络切片的安全部署。仿真结果表明,所提算法在满足端到端网络切片安全部署的同时,降低了部署的成本,获得了较好的部署安全收益。     

End-to-end fair rate optimization in wired-cum-wireless networks

In this paper, we address the end-to-end rate optimization problem in a wired-cum-wireless network, where CSMA/CA based wireless LANs extend a wired backbone and provide access to mobile users. The objective is to achieve proportional fairness amongst the end-to-end sessions in the network. Since the network contains wireless links whose attainable throughput is a (non-convex and non-separable) function of MAC protocol parameters, the problem requires joint optimization at both the transport and the link layers. A dual-based algorithm is proposed in this paper to solve this cross-layer rate optimization problem. It is implemented in the distributed manner, and works at the link layer to adjust scheduling rates for the wireless links in the basic service sets, and at the transport layer to adjust end-to-end session rates. We prove rigorously that the proposed algorithm converges to the globally optimal rates. Simulation results are provided to support our conclusions.     

Network delay tomography

The substantial overhead of performing internal network monitoring motivates techniques for inferring spatially localized information about performance using only end-to-end measurements. In this paper, we present a novel methodology for inferring the queuing delay distributions across internal links in the network based solely on unicast, end-to-end measurements. The major contributions are: 1) we formulate a measurement procedure for estimation and localization of delay distribution based on end-to-end packet pairs; 2) we develop a simple way to compute maximum likelihood estimates (MLEs) using the expectation-maximization (EM) algorithm; 3) we develop a new estimation methodology based on recently proposed nonparametric, wavelet-based density estimation method; and 4) we optimize the computational complexity of the EM algorithm by developing a new fast Fourier transform implementation. Realistic network simulations are carried out using network-level simulator ns-2 to demonstrate the accuracy of the estimation procedure.     

An Optimal Wake-Up Scheduling Algorithm for Minimizing Energy Consumption While Limiting Maximum Delay in a Mesh Sensor Network

This paper presents an algorithm for maximizing the lifetime of a sensor network while guaranteeing an upper bound on the end-to-end delay. We prove that the proposed algorithm is optimal and requires simple computing operations that can be implemented by simple devices. To the best of our knowledge, this is the first paper to propose a sensor wake-up frequency that depends on the sensor's location in the routing paths. Using simulations, we show that the proposed algorithm significantly increases the lifetime of the network while guaranteeing a maximum on the end-to-end delay.     

基于深度确定性策略梯度的虚拟网络功能迁移优化算法

针对NFV/SDN架构下,服务功能链(SFC)的资源需求动态变化引起的虚拟网络功能(VNF)迁移优化问题,该文提出一种基于深度强化学习的VNF迁移优化算法。首先,在底层CPU、带宽资源和SFC端到端时延约束下,建立基于马尔可夫决策过程(MDP)的随机优化模型,该模型通过迁移VNF来联合优化网络能耗和SFC端到端时延。其次,由于状态空间和动作空间是连续值集合,提出一种基于深度确定性策略梯度(DDPG)的VNF智能迁移算法,从而得到近似最优的VNF迁移策略。仿真结果表明,该算法可以实现网络能耗和SFC端到端时延的折中,并提高物理网络的资源利用率。     

Virtual network function deployment strategy based on improved genetic simulated annealing algorithm in MEC

In order to effectively improve the end-to-end service delay of the flow in multi-clusters coexisting mobile edge computing (MEC) network,a virtual network function deployment strategy based on improved genetic simulated annealing algorithm was proposed.The delay of mobile service flow was mathematically modeled through the open Jackson queuing network.After proving the NP attribute of this problem,a solution combining genetic algorithm and simulated annealing algorithm was proposed.In this strategy,the advance mapping mechanism avoids the possibility of network congestion,and the occurrence of local optima was avoided through using the methods of individual judgment and corrective genetic.Extensive simulation was set up to evaluate the effectiveness of the proposed strategy under different parameter settings,such as different volume of requests,different scale of service nodes,different number of MEC clusters,and logical link relationships between virtual network functions.Results show that this strategy can provide lower end-to-end services delay and better service experience for latency-sensitive mobile application.