Linux下Anycast路由协议的设计与实现

讨论了在Linux内核中Anycast路由协议的设计方案和实现技术,将Anycast路由分为内核Anycast路由数据处理、Anycast路由映射表信息的动态交换与更新、主机Anycast组成员信息管理3部分.内核Anycast路由数据处理部分的实现包括Anycast路由软件的设计和Anycast路由过程中的Anycast地址映射算法及映射表维护,在该部分增加了一个核心数据结构即映射表,完成了Anycast地址到Unicast地址的转换.实验结果表明:通过把Anycast路由映射表信息交换系统设计成守护进程,实现了内核映射表信息的动态更新和路由器间映射表信息的交换;通过Anycast组成员之间的信息通信和用户介入控制的方式实现了Anycast组成员的动态管理;采用全IPv6地址和兼容IPv4的IPv6地址进行组管理、映射表信息交换和Anycast路由以及基于HTTP应用的服务测试,可以有效地实现Anycast数据报的转发.     

路由器在广域网中的应用

从路由器的基本原理出发，对路由协议、路由算法进行了全面的分析，并对路由协议、路由算法的选择进行了相应探讨。同时简要介绍了路由配置方法、配置内容及综合测试方法。     

基于智能蚂蚁算法的路由选择

智能蚂蚁算法已经成功地运用于TSP问题和一系列的离散优化问题．文中分析了智能蚂蚁算法的工作原理．并基于蚂蚁的自组织能力描述了一种新的分布式动态路由选择方法，QoS保证的分布式路由选择算法(DQRA)。DQRA能以并行的方式解决网络中呼叫的时延和带宽分配问题。理论证明此种算法简单，易于实现．能有效地解决大型网络的负载平衡问题。选择Matlab仿真软件对DQRA算法进行仿真，并且比较了当业务要求带宽变化时阻塞率的变化．进一步证明了算法的有效性。     

下一代互联网中的智能QoS组播路由算法

设计了一种下一代互联网中的智能QoS组播路由算法,给定一个QoS组播请求和柔性QoS需求,包括带宽需求、组播端到端延迟区间,延迟抖动区间和出错率区间,寻找一棵QoS组播路由树.提出一种基于演化算法和单纯形算法的混合算法来构造满足上述需求且费用近优QoS柔性组播路由树,兼顾网络负载均衡.仿真结果表明,该算法是可行和有效的,明显优于基于传统遗传算法的QoS组播路由算法.     

基于二维路由的流量工程解决方案

流量工程是应对互联网规模和流量高速增长的必要方法,高效的流量工程受限于流量矩阵的测量和已有路由设备转发能力.结合上述挑战提出了基于大流调度的二维路由流量工程方案,建立了描述二维路由大流调度问题的数学优化模型,使用改进的随机取整算法求得问题的近似解.实现了基于Quagga的二维路由大流调度的原型系统,使用扩展链路状态通告下发路由决策信息.真实实验证明,系统能够实现大流的快速调度.仿真实验结果表明,基于二维路由的大流调度明显优于基于传统一维路由方式的大流调度,并且前者能够达到接近最优的调度结果.真实实验和仿真实验证明,该方案不仅仅实现了更为细粒度的流量调度,而且具有更好的可扩展性以及更低的转发流表开销.     

卫星网络中基于蚁群优化的概率路由算法

为了提高空间信息传输的有效性和可靠性,针对传统蚁群优化(ant colony optimization,ACO)容易造成最优路径负载过重而发生拥塞的问题,提出了一种基于蚁群优化的概率路由算法(ant colony optimization based proba-bilistic routing algorithm,ACO-PRA).根据卫星网络拓扑动态周期时变的固有特点,将拓扑周期均匀分为若干个时间片,形成基于不同时间片的卫星网络拓扑连通图;根据网络拓扑连通图,将星间链路带宽和链路容量引入到目标函数中,建立时延最小的优化模型;根据蚁群算法的节点概率函数选择下一跳节点,进而找到一条能同时满足时延带宽和链路容量要求的最佳信号传输路径.仿真结果表明,提出的基于蚁群优化的概率路由算法不仅能够降低平均端到端时延和丢包率,而且能够有效地提高网络吞吐量、平衡网络负载.     

带时间窗的快递包装回收车辆路径优化研究

为解决大量快递包装给社会环境带来的一系列问题,优化快递包装及回收车辆路径,降低回收成本,提高客户满意度是快递包装回收的关键。考虑到快递包装回收的现状,提出在回收过程中设置软时间窗来反映顾客的满意度。建立了运输成本、处理成本、回收成本、时间惩罚成本等成本最小化的数学模型。采用改进的遗传算法对模型求解,提出运用2点交叉法改进交叉算子,加快种群的寻优速度,同时提出2点互异改进变异算子,增加种群的多样性,并对算法的种群规模、变异概率、运输成本等进行了灵敏度分析。运用Matlab软件对算例进行仿真,在有无时间窗条件下,验证了该设计的快递包装回收路径模型和算法的有效性。     

The Capacitated Team Orienteering Problem: A Bi-level Filter-and-Fan method

This paper focuses on vehicle routing problems with profits and addresses the so-called Capacitated Team Orienteering Problem. Given a set of customers with a priori known profits and demands, the objective is to find the subset of customers, for which the collected profit is maximized, and to determine the visiting sequence and assignment to vehicle routes assuming capacity and route duration restrictions. The proposed method adopts a hierarchical bi-level search framework that takes advantage of different search landscapes. At the upper level, the solution space is explored on the basis of the collected profit, using a Filter-and-Fan method and a combination of profit oriented neighborhoods, while at the lower level the routing of customers is optimized in terms of traveling distance via a Variable Neighborhood Descent method. Computational experiments on benchmark data sets illustrate the efficiency and effectiveness of the proposed approach. Compared to existing results, new upper bounds are produced with competitive computational times.     

A Study of the Connectivity Over Time Behavior of On-Demand Ad Hoc Path Selection Schemes

Selecting the most appropriate path between each pair of nodes in an ad hoc network is an issue with major impact on network performance. Many schemes were proposed and compared in the literature, using various criteria. However, the connectivity over time behavior of these schemes, which is important to some practical applications, was not well studied, especially with regards to the terrain type. In this work we use simulation to study this aspect of network performance. We demonstrate that a different connectivity requirement and a different time horizon may dictate a different scheme to use. We also demonstrate that path selection schemes are not equally sensitive to the terrain.     

Combined Path and Maximum Empty Static Routing Algorithms with Wavelength Assignment for Future Transport Networks

Abstract

In this article, two static routing algorithms have been proposed and compared to some of the existing algorithms on the basis of blocking probability. The two proposed static routing and wavelength assignment algorithms reduce the blocking probability to maximize the utilization of the network. All of these algorithms are analyzed and compared with four wavelength assignment schemes, which are first-fit, random, most used, and least used. It is shown that our proposed static algorithms give the best performance for first-fit wavelength assignment and most used wavelength assignment strategies with reduced complexity. For least used wavelength assignment and random wavelength assignment, 1 fixed and 2 alternate routing algorithm gives the lowest blocking probability.