网管系统在企业网中的应用

为了提高基础网络的稳定性和安全性,对网络设备进行有效的监控和管理,我们一般采用网管系统来解决此问题。网管系统一般是通过SNMP协议来对网络设备进行管理,然后将设备状态集中展示,当网络设备出现问题进行告警,以便网络管理人员能够快速处理故障,提高网络整体可用性。     

基于SNMP的物理网络拓扑发现的实现

网络拓扑信息对于网络管理是至关重要的。使用简单网络管理协议SNMP可以获取存放在网络设备的管理信息库MIB中的信息。根据MIB中的地址转发表,判断各网络设备之间的端口链接,从而可以得到局域网的网络拓扑结构。实验表明,该过程有效、可用,能够准确发现局域网的拓扑结构。     

基于XML网络管理中多协议转换网关的研究

论文介绍了多协议转换网关的转换算法及系统模型,并利用转换网关对基于XML的网络管理通信模型进行了改进。改进后的基于XML的网络管理系统不但可以管理基于Web代理的网络设备,而且可以管理传统的基于SNMP代理的网络设备及基于Telnet(SSH,SSL)代理的网络设备。     

一种基于WebGIS的网络拓扑管理技术

将地理信息系统与网络拓扑管理结合,能够实现对网络设备故障快速直接的地理定位,赋予网管系统清晰直观、易于监控和管理的特性。提出了一种基于网络地理信息系统的层次化拓扑发现和拓扑显示技术,有效地实现了网络拓扑和地理信息系统的结合,实现了网络设备的对象化管理,实现了网络设备故障的快速直接的地理定位,为网络管理提供了新型的地理化的操作界面,为网络故障管理的研究应用提供了新的有效手段,并且可以使网络管理员随时随地对网络进行管理。     

基于WBEM存储网络管理中设备的自动发现

存储网络是由各种存储设备和网络设备组成的面向数据存储的专用网络系统。存储网络中的设备具有很大的动态性,因此,如何获得网络中设备的状态和性能是实现存储网络管理中的一个最基本要求。论文基于WBEM管理框架,引入受管资源注册以及SIP中的注册机制,提出并实现一种存储网络设备的自动发现方法。测试结果表明,论文所提出的设备自动发现算法具有较好的性能和应用价值。     

一种基于WebGIS的网络拓扑管理技术

将地理信息系统与网络拓扑管理结合,能够实现对网络设备故障快速直接的地理定位,赋予网管系统清晰直观、易于监控和管理的特性.提出了一种基于网络地理信息系统的层次化拓扑发现和拓扑显示技术,有效地实现了网络拓扑和地理信息系统的结合,实现了网络设备的对象化管理,实现了网络设备故障的快速直接的地理定位,为网络管理提供了新型的地理化的操作界面,为网络故障管理的研究应用提供了新的有效手段,并且可以使网络管理员随时随地对网络进行管理.     

多校区校园网络的资源共享与管理

多校区校园网的升级改造是很多学校面临的问题,本文针对我校具体情况介绍网络无缝连接的方式,网络设备选择及架构,网络管理策略,从而能够更好地服务于教育教学。     

方正网略 网络管理的利器

随着企业信息化建设的趋向成熟,网络设备和计算机数量不断增长,网络规模日趋庞大,从而对网络管理也提出了更高的要求。 目前市场上的网络管理软件基本上都遵循国际标准化组织(ISO)的标准,可以实现 1、配置管理:发现、维护和配置网络拓扑和设备的过程 2.性能管理:通过采集和测量网络     

Cisco路由器流量采集软件的研制

网络流量能反映网络的运行状态,是网络管理领域中重要的研究课题。随着网络技术的发展,网络应用的不断深入,网络的有效性、安全性分析尤显重要,直接分析网络实时流量,将具有非常重要的意义。NetFlow是Cisco公司为实现流量统计和流量分析而开发的一种协议,使用NetFlow可以方便地进行大规模的流量采集。通过对NetFlow协议原理与功能的分析,可以设计一种NetFlow流量分析器,该分析器能够对NetFlow流量数据进行采集、分析和显示,实现网络的异常流量监测、流量统计和综合流量分析,并能及时地发现网络攻击。     

多校区校园网络的资源共享与管理

多校区校园网的升级改造是很多学校面临的问题，本文针对我校具体情况介绍网络无缝连接的方式，网络设备选择度架构，网络管理策略。从而能够更好地服务于教育教学。     

A biased random-key genetic algorithm for wireless backhaul network design

This paper describes a biased random-key genetic algorithm for a real-world wireless backhaul network design problem. This is a novel problem, closely related to variants of the Steiner tree problem and the facility location problem. Given a parameter h, we want to build a forest where each tree has at most h hops from the demand nodes, where traffic originates, to the root nodes where each tree is rooted. Candidate Steiner nodes do not have any demand but represent locations where we can install cellsites to cover the traffic and equipment to backhaul the traffic to the cellular core network. Each Steiner node can cover demand nodes within a given distance, subject to a capacity constraint. The aggregate set of constraints may make it impossible to cover or backhaul all demands. A revenue function computes the revenue associated with the total amount of traffic covered and backhauled to the root nodes. The objective of the problem is to build a forest that maximizes the difference between the total revenue and the cost associated with the installed equipment. Although we will have a forest when we consider only the backhaul links and root nodes, the addition of demand vertices can induce undirected cycles, resulting in a directed acyclic graph. We consider instances of this problem with several additional constraints that are motivated by the requirements of real-world telecommunication networks.     

Web集群远程监控策略

针对Web集群存在的网络流量监控问题,构建MRTG流量监控,根据Web服务的网络流量特点设置远程流量监控参数,使之能够监控集群中多台设备,通过Apache发布直观可靠的图形化网络流量和设备性能数据,研究MRTG在实际应用中的缺陷,提出合理可行的改进策略,使网络管理人员能够更加有效的通过远程监控网络流量和性能负载,判断网路或设备发生问题的可能原因,快速定位并加以处理。     

网络生存性综述

当网络内出现故障时,网络维持服务连贯性的能力称之为生存性。在当前多层次的流量工程化IP网络中考虑生存性已经变得重要而且紧急。分析并比较了网络各层次中的生存性机制,包括WDM层、SDH层、1ATM层、MPLS层和IP层生存性机制的相关概念、分类和研究现状。最后介绍了在层间调和各种生存性机制的可行性与利弊。在进行流量工程设计的过程中适当地采用这些生存性机制,可以更好地实现流量工程目标。     

基于PF-LSTM网络的高效网络流量预测方法

传统网络流量预测模型存在泛化能力弱和预测准确度低等缺点。为解决此问题,提出一种基于长短期记忆（LSTM）循环神经网络的网络流量预测模型。首先,用距离比较和优化组合策略改进粒子滤波算法（PF）的重采样过程;然后搭建PF-LSTM网络的网络流量预测模型,将改进的PF算法用于模型训练,提高其训练速率,克服传统LSTM网络中收敛于局部最优的缺点;最后将提出的模型用于网络流量预测。实验结果表明,相比传统的LSTM模型,提出的PF-LSTM模型具有较好的预测精度和收敛效率,能够更好地描述网络流量的变化趋势。     

Implementing a generalized tool for network monitoring

Determining how you were attacked is essential to developing a response or countermeasure. Usually, a system or network manager presented with a successful intrusion has very little information with which to work: a possibly corrupted system log, a firewall log, and perhaps some tcpdump output.When hackers come up with a new technique for cracking a network, it often takes the security community a while to determine the method being used. In aviation, an aircraft's ‘black box’ is used to analyze the details of a crash. We believe a similar capability is needed for networks. Being able to quickly learn how an attack works will shorten the effective useful lifetime of the attack. Additionally, the recovered attack records may be helpful in tracking or prosecuting the attacker. Since we have developed a general purpose statistics-gathering system, we believe it will be useful for more than just security. For example, a network manager may desire an historical record of the usage growth of certain applications, or details about the breakdown of types of traffic at different times of day. Such records will provide useful information for network managers in diagnosing performance problems or planning growth.This paper describes an architecture and toolkit for building network traffic analysis and statistical event records: The Network Flight Recorder. The NFR uses a promiscuous packet interface to pass visible traffic into an internally meta-programmed decision engine which routes information about packets and their contents into statistical or logging backends. In addition to packet analysis and collection, the NFR's internal architecture permits network managers to sample interesting portions of network traffic for logging or statistical analysis. The NFR programming language is simple, but powerful enough that you can perform reasonable analysis on traffic before choosing to record it. For example, you might analyze SMTP transactions but only choose to record those relating to a user who is sending spam or abusive E-mail. The analysis language includes a capability for generating alert messages which the rest of the system queues, multiplexes, and delivers. A simplified hyper-query interface allows extensive browsing of the NFR's stored datasets and statistics from any Java-enabled browser. The NFR is currently being deployed at a number of ISPs and commercial sites, and is available for download in source code form from www.nfr.net.     

针对设备端口链路的LSTM网络流量预测与链路拥塞方案

针对设备端口链路流量,提出两种基于长短期记忆网络的预测模型。第一种针对在大时间粒度下平稳变化的流量;第二种则针对在小时间粒度下波动剧烈的非平稳流量。通过选用不同的数据划分方式与模型训练方法,构建两种具有不同网络结构的流量预测模型。实验结果表明,前者在处理平稳变化的流量时能够达到极高的预测精度,后者在处理非平稳流量时具有明显优于SVR模型、BP神经网络模型的预测效果。在第二种预测模型的基础上,提出了参数可调的链路拥塞预警方案,实验证明该方案具有一定的可行性。     

基于BP神经网络的非线性网络流量预测

传统的流量分析建立在线性模型的基础上,但是由于复杂的拓扑结构和网络行为,网络流量表现为一个非线性的系统。根据实际网络中测量的大量网络流量数据,建立一个时间相关的基于神经网络的流量模型,预测和分析网络流量状况。相对于传统线性模型该模型具有较高的预测精度、自适应性和鲁棒性。     

On the network impact of dynamic server selection

Widespread replication of information can ameliorate the problem of server overloading but raises the allied question of server selection. Clients may be assigned to a replica in a static manner or they may choose among replicas based on client-initiated measurements. The latter technique, called dynamic server selection (DSS), can provide significantly improved response time to users when compared with static server assignment policies (for example, based on network distance in hops).In the first part of this paper we demonstrate the idea of DSS using experiments performed in the Internet. We compare a range of policies for DSS and show that obtaining additional information about servers and paths in the Internet before choosing a server improves response time significantly. The best policy we examine adopts a strategy of never adding more than 1% additional traffic to the network, and is still able to provide nearly all the benefits of the most expensive policies.While these results suggest that DSS is beneficial from the network user's standpoint, the system-wide effects of DSS schemes should also be closely examined. In the second part of this paper we use large-scale simulation to study the system-wide network impact of dynamic server selection. We use a simulated network of over 100 hosts that allows local-area effects to be distinguished from wide-area effects within traffic patterns.In this environment we compare DSS with static server selection schemes and confirm that client benefits remain even when many use DSS simultaneously. Importantly, we also show that DSS confers system-wide benefits from the network standpoint, as compared to static server selection. First, overall data traffic volume in the network is reduced, since DSS tends to diminish network congestion. Second, traffic distribution improves – traffic is shifted from the backbone to regional and local networks.     

基于改进Elman神经网络的网络流量预测

针对网络系统非线性、多变量、时变性等特点,提出一种改进的Elman神经网络模型。在该模型的训练过程中引入了季节周期性学习方法,并对某高校主干网络出口流量进行实验检测。实验结果表明,该模型具有良好的预测效果,相对于传统线性模型、BP神经网络模型及标准Elman神经网络模型具有更高的预测精度和更好的自适应性。最后,通过自适应边界值方法进行检测,能够及时发现异常流量行为,说明该模型应用于网络流量预测是可行、有效的。     

LVQ神经网络在交通事件检测中的应用

提出一种基于LVQ神经网络的交通事件检测方法。提取上下游的流量和占有率为特征,LVQ神经网络作为分类器进行交通事件自动检测。LVQ网络结构简单,但却表现出比BP神经网络更强的有效性和鲁棒性。为进一步提高神经网络的泛化能力,采用改进的Boosting算法,进行网络集成。运用Matlab 进行了仿真分析,结果表明提出的交通事件检测算法具有良好的检测性能。