基于双层递归神经网络模型求解最优组播路由

基于覆盖网络的组播作为一种新的IP网络组播解决方案已得到广泛关注。提出了一种利用改进的双层递归神经网络模型求解VPON网络环境下的QoS（服务质量）最优组播路由的方案。该方案在选择路由时综合考虑链路的可用带宽及节点的剩余处理能力，并运用一种基于改进的双层递归神经网络模型——MTLRNN进行求解，与其它启发式组播路由算法相比，该方案在满足应用的QoS要求的前提下，使全网的负载分配更加均衡，同时在解的有效性及接纳的组播应用会话数方面都有比较大的改善。     

一种基于计算智能的组播路由算法

在通信网络中,多约束组播通信是提高网络运行效率和服务质量的重要途径。一些启发式的算法已经被用来解决多约束条件下的组播路由问题,如模拟退火算法,遗传算法,蚁群算法和粒子群优化算法等。然而,这些算法在求解多约束组播路由问题时存在收敛速度低和计算复杂度高的问题。萤火虫群优化(GSO)算法是一种近期在计算智能领域出现的卓越算法,它可以在一定程度上解决多约束组播树生成过程中收敛速度低和计算复杂度高的问题。提出了一种基于GSO的多约束组播树生成算法(GSO-MCM)。该算法可有效生成满足多约束要求的组播路由树。仿真结果表明提出的GSO-MCM算法在求解和收敛速度,以及网络规模适应性方面均有良好的性能。     

光组播路由代价与波长使用量的联合优化方法

为解决光组播路由中组播中路由代价和波长资源消耗单一化造成的组播路树路由的代价过高问题,在分光节点约束条件下,提出了光组播路由代价与波长使用量联合优化的长路优先(LPF)方法和短路优先(SPF)方法。算法通过检查最小光组播树是否存在节点分光约束的问题,根据设置的波长使用代价控制因子,使LPF或SPF的路由代价和波长使用量最小。LPF方法首先选择组播树最长路径或新波长通道重路由受分光约束的目的节点,SPF方法先选择组播树中最短路径或新波长通道重路由受分光约束的目的节点,仿真结果表明,本文提出的两种联合优化方法都能实现路由代价较低和波长需求较少的目的。     

改进遗传算法在QoS组播路由选择中的研究

QoS组播路由的选择问题本质是从源节点至目的节点间找到满足QoS要求的传输路径,尽管现在有许多文献利用遗传算法对组播路由问题做了研究,但其组播路由费用都比较大,执行时间过长,并且编码难度都比较大,优化效率低。针对这些问题,文中提出了一种改进的遗传算法,该算法综合考虑了带宽约束、时延约束、时延抖动约束、丢包率约束以及路由费用等度量指标,在传统遗传算法的基础上,设计了一种基于应用层业务的自适度函数,改进了染色体编码方式,并且设计了自适应交叉函数与自适应变异函数等多种优化函数。通过仿真结果表明,该改进的遗传算法很大程度上降低了QoS组播路由的费用和执行时间,并且能快速地找到最优QoS组播路径。     

求解度约束组播路由的新型蚁群算法

 基于蚁群算法的正反馈机制提出了一种基于树的蚁群算法,并用它来求解度约束组播路由问题.在该算法中,蚂蚁按照一定的概率选择一条链路加入组播子树,然后检查加入点的度约束情况,如果该点的度约束情况达到饱和,则蚂蚁以后不再选取与该点连接的链路.通过计算模拟分析方法证明了该算法的有效性,计算机仿真结果显示,在解决度约束组播路由问题时,该新型蚁群算法的收敛速度大大快于已有的蚁群算法,找到的最优解性能稍好于已有的算法,算法的空间复杂度也得到降低.     

IP组播技术初探

主要讨论IP组播的路由选择问题，指出了路由选择的目的地址问题和回路问题的解决方法。     

QoS组播路由的启发式遗传算法

本文首先提出带宽延时约束、费用最小QoS组播路由模型,然后提出解决QoS组播路由问题的一种启发式遗传算法,该算法有以下特点:(1)预处理机制;(2)树结构编码;(3)启发式交叉策略;(4)指导性变异过程.最后通过仿真实验证明该算法快速有效.     

基于遗传算法优化的QoS组播路由算法

QoS组播路由技术作为网络多媒体信息传输的一种核心技术,下一代网络中的QoS组播路由的优化算法已经成为目前研究的一个热点。针对QoS组播的网络模型,提出一种适用于下一代网络的组播路由算法能够更好的满足下一代网络的QoS组播需求显得尤为重要。本文讨论了遗传算法优化的QoS组播路由算法,最后探讨了下一代网络QoS组播路由算法的研究方向。     

量子进化组播路由算法

近年来,时延受限的代价最小组播树问题备受关注。作为全局优化算法,遗传算法（GA）越来越多的用于解决组播路由问题。GA拥有比经典算法更强的搜索能力,但是它容易陷入＂早熟＂,很难得到最优组播树。基于量子计算的机理和特性并结合进化计算,提出了一种新颖的量子进化组播路由算法（QEA）,有效地解决了遗传组播路由算法中的＂早熟＂问题,并且在每代个体更新中采用量子旋转门策略加速了算法的收敛速度。算法实现简单,控制灵活。仿真结果表明QEA算法性能优于改进的进化算法即克隆多播路由算法（CS）和传统的遗传算法（GA）。     

基于共享路径和网络编码的光组播容量优化

为了降低光组播路由 的光域网络编码代价和提高达到理论最大光组播容量的 概率,提出一种基于共享链路和网络编 码的优化光组播容量方法。首先设计一种从多条源－ 宿最短路径中选择能达到最大光组播容量的最短路径簇,然后在 最短路径簇中计算路径的共享度,选择共享度高的组播路径传输网络编码信息,构造网络编 码次数最少的光组播编码子图, 解决传统的网络编码组 播路由和最大共享度链路组播路由中存在的网络编码次数过多和达到最大光组播容量概率过 低的问 题。仿真结果表明:本文提出的方法具有最低的网络编码代价,能以最大的概率达到光组播 理论最大容量。     

动态QoS多播路由协议

本文主要研讨了具有QoS约束的动态多播路由问题.文中描述了一种适用于QoS多播路由的网络模型,提出了一种动态QoS多播路由协议(DQMRP),该协议能操作在单播路由协议的顶层,它只要求网络链路(或节点)的局部状态信息,不需要维护全局状态信息.DQMRP可有效地减少构造一棵多播树的开销,多播组成员可动态地加入／退出多播会晤.该协议可搜索多条可行树枝,并能选择一条最优(或近优)树枝将新成员连接到多播树.文中给出了DQMRP的正确性证明和复杂性分析,并通过仿真实验验证了该协议的可用性和有效性.     

平面网络中QOS多播路由算法研究与设计

多播通信这种兼顾了广播通信与单播通信两者优点的通信模式,已经被越来越多地应用于平面网络业务之中。首先讨论了面向源节点的多播路由策略。在分析传统多播路由算法中最佳链路选择函数的基础上,提出了一个新的最佳路由模型和QMRI算法,该算法成功地解决了网络中多播节点动态变化时的QOS路由选择问题。     

WDM疏导网络中一种新的多播业务路由算法

研究了波分复用(WDM)网状网中动态多播业务量疏导,提出一种新的辅助疏导模型,其可以描述当前网络资源状况和节点分光特点,并动态更新.进而提出一种有效的多播业务量疏导启发式算法(MGA),将业务的多播选路和波长分配同时完成.仿真表明,该算法在波长连续性限制、网络波长和节点收发器数目有限的情况下,具有较低网络阻塞率.     

Multicast routing and bandwidth dimensioning in overlay networks

Multicast services can be provided either as a basic network service or as an application-layer service. Higher level multicast implementations often provide more sophisticated features and can provide multicast services at places where no network layer support is available. Overlay multicast networks offer an intermediate option, potentially combining the flexibility and advanced features of application layer multicast with the greater efficiency of network layer multicast. In this paper, we introduce the multicast routing problem specific to the overlay network environment and the related capacity assignment problem for overlay network planning. Our main contributions are the design of several routing algorithms that optimize the end-to-end delay and the interface bandwidth usage at the multicast service nodes within the overlay network. The interface bandwidth is typically a key resource for an overlay network provider, and needs to be carefully managed in order to maximize the number of users that can be served. Through simulations, we evaluate the performance of these algorithms under various traffic conditions and on various network topologies. The results show that our approach is cost-effective and robust under traffic variations.     

曙光5000高性能计算机多播网络的设计

 本文介绍了曙光5000高性能计算机多播网络设计的关键技术.减少多播与单播/多播与多播间的网络竞争是降低多播延迟的主要途径之一,而无死锁则是保障多播操作正确完成的前提.为了解决死锁问题,本文提出了一种基于全局资源公告的死锁避免方法;为了获得较低的多播延迟,本文充分利用胖树拓扑特点,提出了一种基于重载交换机去除的多播路径选择策略.测试结果表明,在网络重载情况下,相比于已有多播路径选择算法,本文的路径选择策略可以获得近三倍的性能提升.对于many-to-many多播通信,曙光5000多播网络可以获得90%以上的多播吞吐率.     

基于灰狼算法的无线网络组播路由优化

针对无线网络中资源受限的组播路由问题,考虑网络节点的节点度限制和网络链路的带宽约束,以最小化组播路由开销为目标,提出了一种二进制编码方式的基于灰狼优化算法的组播路由策略.在给定的网络拓扑下,基于灰狼优化算法的组播路由策略可以迅速找到一棵包含源和目的节点的最小开销组播树.仿真结果表明,相比于遗传算法,所提出的基于灰狼优化...     

Dynamic multicast routing based on mean number of new callsaccepted before blocking for single rate loss networks

In this paper, we investigate the dynamic multicast routing problem for single rate loss network and briefly discuss the dynamic multicast routing algorithm called least load multicast routing (LLMR). We propose a new multicast routing algorithm called maximum mean number of new calls accepted before blocking multicast routing (MCBMR), which can more accurately capture the current and future loading of a network. Simulation results show that this algorithm, compared with LLMR, not only has a smaller network revenue loss, but also results in smaller call blocking probabilities for all classes of traffic. We also discuss the implementation issues of our proposed algorithm and develop two approximation methods, state approximation and curve fitting, which can reduce the measurement complexity significantly with only a slight performance degradation     

A Cross-Layer Optimization Framework for Multihop Multicast in Wireless Mesh Networks

The optimal and distributed provisioning of high throughput in mesh networks is known as a fundamental but hard problem. The situation is exacerbated in a wireless setting due to the interference among local wireless transmissions. In this paper, we propose a cross-layer optimization framework for throughput maximization in wireless mesh networks, in which the data routing problem and the wireless medium contention problem are jointly optimized for multihop multicast. We show that the throughput maximization problem can be decomposed into two subproblems: a data routing subproblem at the network layer, and a power control subproblem at the physical layer with a set of Lagrangian dual variables coordinating interlayer coupling. Various effective solutions are discussed for each subproblem. We emphasize the network coding technique for multicast routing and a game theoretic method for interference management, for which efficient and distributed solutions are derived and illustrated. Finally, we show that the proposed framework can be extended to take into account physical-layer wireless multicast in mesh networks     

Multicast wavelength assignment with sparse wavelength converters to maximize the network capacity using ILP formulation in WDM mesh networks

In general, multicast routing and wavelength assignment (MC-RWA) can be subdivided in routing and wavelength assignment issues in wavelength-division multiplexing (WDM) mesh networks. Previous studies on WDM multicast have mainly focused on WDM multicast routing. The multicast wavelength assignment problem is studied in this paper. A unicast routing path can be established by a lightpath in an all-optical network. However, in the multicasting case, a multicast routing tree can be established by a single light-tree or several lightpaths, or a combination of several light-trees and lightpaths. We propose a wavelength assignment algorithm for finding an optimal combination of lightpaths and light-trees to construct a newly required multicast session. First of all, two cost functions are given to evaluate the establishing cost for each feasible wavelength, and then find a set of wavelengths that covers all destinations with the minimal cost using Integer Linear Programming (ILP) formulation. We focus on maximizing the total number of users served in a multicast session and the network capacity. The simulation results show that the proposed algorithm can improve system resource utilization and reduce the blocking probability compared with the First-Fit algorithm.This research was partially supported by the Grant of National Science Council, R.O.C. (NSC 94-2745-E-155-007-URD).     

基于蚂蚁算法的多约束QOS组播路由选择

描述了多约束QoS组播路由问题的网络模型,提出了一种解决该问题的改进的蚂蚁算法.该算法对网络进行预处理,生成初始解,并转化为网络的初始信息素分布,利用蚂蚁算法的正反馈特性调整信息量的分配,使之迅速收敛到问题的最优解.仿真表明,算法可以稳定地获得优于现有启发式算法的解,是一种有效的组播路由算法.