一类基于随机行走机理的优化路由改进策略

在对随机行走过程的研究中发现:单个粒子通过某条特定路径的时间正比于该路径上所有节点度的连乘积.据此,文章提出基于随机行走机理的优化路由改进策略.该策略以节点度连乘积最小化为原则,通过调节可变参数,建立节点处理能力均匀分布的情况下最佳路由策略.通过分析比较不同路由策略条件下平均路由介数中心度,网络的临界负载量,平均路径长度以及平均搜索信息量等性能指标,研究结果表明,此改进路由策略在保证网络平均路径长度较少增加的前提下,使网络的传输能力获得最大幅度的提升. 关键词： 复杂网络 路由策略 负载传输     

基于社团结构的负载传输优化策略研究

研究表明网络社团结构特征对负载传输有影响,明显社团结构特征会降低网络的承载能力.由于最短路由策略在选择路由时有一定的随机性,本文提出了一种基于社团结构的负载传输策略,减少最短路由经过的社团数量,从而降低社团边缘节点的介数.实验结果显示,该策略在保证最短路由小世界特性的同时,提升了网络的承载能力,社团划分得越准确传输优化策略效果越显著. 关键词： 优化路由策略 社团结构 复杂网络 负载传输     

一种有效提高无标度网络负载容量的管理策略

现有研究表明明显的社团结构会显著降低网络的传输性能. 本文基于网络邻接矩阵的特征谱定义了链路对网络社团特性的贡献度, 提出一种通过逻辑关闭或删除对网络社团特性贡献度大的链路以提高网络传输性能的拓扑管理策略, 即社团弱化控制策略(CWCS 策略). 在具有社团结构的无标度网络上分别进行了基于全局最短路径路由和局部路由的仿真实验, 并与关闭连接度大的节点之间链路的HDF 策略进行了比较. 仿真实验结果显示, 在全局最短路径路由策略下, CWCS策略能更有效地提高网络负载容量, 并且网络的平均传输时间增加的幅度变小. 在局部路由策略下, 当调控参数0<α<2, 对网络负载容量的提升优于HDF策略. 关键词： 复杂网络 社团特性 负载容量 拓扑管理     

一种复杂网络路由策略的普适优化算法

现有的复杂网络路由策略很多,改进算法也不断涌现,但是目前还没有一个统一的标准来衡量算法是否达到网络最佳传输效果.针对这一问题,本文提出一种适用于现有路由策略的普适优化算法.首先通过理论分析指出制约网络传输能力的关键因素是最大介数中心度,因而"最大介数中心度是否已经最低"成为评判路由策略是否最优的标准.在此基础上,采用"惩罚选择法"避开网络中介数中心度值比较大的节点,使网络介数中心度值分布更均匀,均衡网络中各个节点的传输负载.仿真结果显示,该优化算法针对现有路由策略均能降低最大介数中心度值,大幅度提高网络的传输能力.     

复杂网络的一种加权路由策略研究

复杂网络的传输能力是其功能正常运转的重要保障,提高网络的吞吐量有着重要意义.提出一种新的高效路由策略，以提高复杂网络的传输能力，称之为加权路由策略.即对网络的每一条边加权，权值与该边的两端节点的度相关，然后数据包按照这个加权网络的最短路径路由.这样的路径可以更均匀地经过各个节点，发挥它们的传输能力，极大地提高网络的吞吐量.可以避免数据包集中地通过个别度大的节点，在这些节点发生拥塞.仿真显示，该策略比传统的最短路径策略优越，对很多结构的网络，可以提高几十倍的吞吐量. 关键词： 复杂网络 路由策略 吞吐量 拥塞     

基于最短路径长度的空间网络路由

以通信网、电力网、交通网为代表的很多复杂网络以传输负载为基本功能.在这些网络中,网络的吞吐量是衡量网络传输性能的重要指标,如何提升网络的吞吐量是研究热点之一.不少研究人员提出了不同的路由算法,通过调节传输路径来提高网络吞吐量.但之前的研究很少考虑网络中节点的空间位置.本文针对空间网络提出了一种高效的路由策略,通过节点位置得到路径长度;采用该算法,负载从源节点沿着最短长度的路径传输到目标节点.为了检验算法的有效性,采用网络从自由流状态转变成拥塞状态的相变点Rc来衡量网络的吞吐量.在匀质和异质空间网络上的仿真表明,与传统的最少跳数路由策略相比,本文提出的基于最短路径长度的路由算法能有效提高空间网络的吞吐量.     

基于局部路由策略的复杂网络拥塞控制

提出一种复杂网络上的局部路由策略,算法采用节点收缩法评估节点的重要度,发送节点根据邻居节点的重要度及网络的状态自适应地调整向邻居节点转发数据包的概率.在网络处于自由流通状态时充分发挥关键节点的优势,保证数据包快速到达目的地;在网络处于即将拥塞时分散业务,根据节点重要度准确识别网络中的关键节点,通过有效分流予以保护.仿真结果表明:在网络处于自由流通状态时,该局部路由策略能充分发挥网络中关键节点的枢纽作用,保持较低的传输时延;在网络部分关键节点出现拥塞时,该局部路由策略能有效避开拥挤严重的节点,将数据包均匀地分布在各个节点上,有效抑制网络拥塞,提高网络的容量.     

基于感知流量算法的复杂网络拥塞问题研究

研究了在具有感知流量的路由策略下，复杂网络的拓扑结构对网络中传输流量的影响.为了描述数据包传输过程的有效性，通过引入一个状态参数，利用由稳态到拥塞的指标流量相变值来刻画网络的吞吐量.基于每个节点的数据包处理能力与该节点的度或介数成比例提出两种模型并进行仿真.仿真结果表明，平均度相同的情况下，模型Ⅰ中，WS小世界网络比ER随机网络和BA无标度网络更容易产生拥塞；模型Ⅱ中，所有网络容量都得到较大的提高，尤其是WS小世界网络.但当网络的基本连接参数改变时，哪种模型更利于网络的流量传输，还要依据网络本身的结构特性 关键词： 复杂网络 无标度网络 感知流量 拥塞     

异质化带宽分配下的复杂网络数据流负载问题研究

研究了带有连接边传输容量(带宽)约束的复杂网络上如何提升网络数据流负载问题. 在网络连接边带宽资源总量固定的条件下, 提出了一种异质化带宽分配方案. 引入 "受控边" 概念, 通过加入适当比例的 "受控边", 重新分配带宽资源, 并结合具有拥塞感知能力路由策略的数据流量模型, 利用带宽分配调节数据流量走向, 提高了带宽利用效率, 最终使得网络整体的负载能力较带宽匀质化分配时有显著提升. 分别在Barabási-Albert无标度网络和Watts-Strogtz (WS)小世界网络平台上仿真, 发现按照本文的带宽分配方案, WS小世界网络中节点连接边带宽与网络负载有较强的相关性, 节点连接边带宽分配最均衡的时候, 网络负载能力达到最大. 关键词： 异质化带宽分配 负载 介数 受控边     

基于耦合强度的双层网络数据传输能力

为了降低网络拥塞,提升网络传输性能,对双层网络之间的耦合机理进行研究,层间关系依据度度相关性分成三种耦合方式:随机耦合、异配耦合、同配耦合.在基于最短路径路由策略和基于度的权重路由策略条件下,分析网络数据包的传输过程,并研究双层网络的耦合方式及其适合的路由策略对网络传输容量的影响.采用双层无标度网络进行仿真实验,分析在路由策略约束下传输容量和耦合方式之间的关系,依据双层网络之间耦合方式的特点,找出适合每一种路由策略的最佳耦合方式以提升网络的传输容量.经过仿真发现,采用最短路径路由策略时,异配耦合方式最佳;采用基于度的静态权重路由策略时,同配耦合方式最佳.路由策略在匹配的耦合方式下使得网络流量分配均匀,有利于网络传输容量的提升.本研究为实际网络设计和传输性能优化提供了理论基础.     

A dynamic routing strategy with limited buffer on scale-free network

In this paper, we propose an integrated routing strategy based on global static topologyinformation and local dynamic data packet queue lengths to improve the transmissionefficiency of scale-free networks. The proposed routing strategy is a combination of aglobal static routing strategy (based on the shortest path algorithm) and local dynamicqueue length management, in which, instead of using an infinite buffer, the queue lengthof each node i in the proposed routing strategy is limited by acritical queue length Q_ic. When the networktraffic is lower and the queue length of each node i is shorter than itscritical queue length Q_ic, it forwardspackets according to the global routing table. With increasing network traffic, when thebuffers of the nodes with higher degree are full, they do not receive packets due to theirlimited buffers and the packets have to be delivered to the nodes with lower degree. Theglobal static routing strategy can shorten the transmission time that it takes a packet toreach its destination, and the local limited queue length can balance the network traffic.The optimal critical queue lengths of nodes have been analysed. Simulation results showthat the proposed routing strategy can get better performance than that of the globalstatic strategy based on topology, and almost the same performance as that of the globaldynamic routing strategy with less complexity.     

Generalized minimum information path routing strategy on scale-free networks

This paper presents a new routing strategy by introducing a tunable parameter into the minimum information path routing strategy we proposed previously.It is found that network transmission capacity can be considerably enhanced by adjusting the parameter with various allocations of node capability for packet delivery.Moreover,the proposed routing strategy provides a traffic load distribution which can better match the allocation of node capability than that of traditional efficient routing strategies,leading to a network with improved transmission performance.This routing strategy,without deviating from the shortest-path routing strategy in the length of paths too much,produces improved performance indexes such as critical generating rate,average length of paths and average search information.     

Optimization of transport protocols in complex networks

In this paper, an optimal routing strategy is proposed to enhance the traffic capacity of complex networks. In order to avoid nodes overloading, the new algorithm is derived on the basis of generalized betweenness centrality which gives an estimate of traffic handled by the node for a route set. Since the nodes with large betweenness centrality are more susceptible to traffic congestion, the traffic can be improved, as our strategy, by redistributing traffic load from nodes with large betweenness centrality to nodes with small betweenness centrality in the proceeding of computing collective routing table. Particularly, depending on a parameter that controls the optimization scale, the new routing can not only enlarge traffic capacity of networks more, but also enhance traffic efficiency with smaller average path length. Comparing results of previous routing strategies, it is shown that the present improved routing performs more effectively.     

Optimal routing strategy based on the minimum information path

In this paper, we propose a new routing strategy based on the minimum information path, named the optimal routing (OR) strategy, to improve the transportation capacity of scale-free networks. We define the average routing centrality degree of the node to analyze the traffic load on nodes of different degree. We analyze the transportation capacity by using the critical values of R_c, the average packet travel time, and the average path length. Both theoretical and experimental results show that the capacity of the network under our strategy will be maximized when the packet-delivery rate of the node is directly proportional to the degree.     

Probability routing strategy for scale-free networks

This study proposes a probability routing strategy for improving traffic capability on scale-free networks. Compared with the shortest path routing strategy depending on central nodes largely and the efficient routing strategy avoiding hub routers as much as possible, the probability routing strategy makes use of hub routers more efficiently, transferring approximate average amount of packs of the whole network. Simulation results indicate that the probability routing strategy has the highest network capacity among the three routing strategies. This strategy provides network capacity that can be more than 30 times higher than that of the shortest path routing strategy and over 50% higher than that of the efficient routing strategy. In addition, the average routing path length of our proposed strategy is over 10% shorter than that of the efficient routing strategy and only about 10% longer than that of the shortest path routing strategy.     

Global forward-predicting dynamic routing for traffic concurrency space stereo multi-layer scale-free network

Many real communication networks, such as oceanic monitoring network and land environment observation network,can be described as space stereo multi-layer structure, and the traffic in these networks is concurrent. Understanding how traffic dynamics depend on these real communication networks and finding an effective routing strategy that can fit the circumstance of traffic concurrency and enhance the network performance are necessary. In this light, we propose a traffic model for space stereo multi-layer complex network and introduce two kinds of global forward-predicting dynamic routing strategies, global forward-predicting hybrid minimum queue(HMQ) routing strategy and global forward-predicting hybrid minimum degree and queue(HMDQ) routing strategy, for traffic concurrency space stereo multi-layer scale-free networks. By applying forward-predicting strategy, the proposed routing strategies achieve better performances in traffic concurrency space stereo multi-layer scale-free networks. Compared with the efficient routing strategy and global dynamic routing strategy, HMDQ and HMQ routing strategies can optimize the traffic distribution, alleviate the number of congested packets effectively and reach much higher network capacity.     

Routing in scale-free networks based on expanding betweenness centrality

In this paper, an improved routing strategy is proposed for enhancing the traffic capacity of scale-free networks. Instead of using the information of degree and betweenness centrality, the new algorithm is derived on the basis of the expanding betweenness centrality of nodes, which gives an estimate of the traffic handled by the vertex for a certain route set. Since the nodes with large betweenness centrality are more susceptible to traffic congestion, the traffic can be improved by redistributing traffic loads from nodes with large betweenness centrality to nodes with small betweenness centrality in the process of computing the collective routing table. Comparing with results of previous routing strategies, it is shown that the present improved routing performs more effectively.     

Adaptive local routing strategy on a scale-free network

Due to the heterogeneity of the structure on a scale-free network,making the betweennesses of all nodes become homogeneous by reassigning the weights of nodes or edges is very difficult.In order to take advantage of the important effect of high degree nodes on the shortest path communication and preferentially deliver packets by them to increase the probability to destination,an adaptive local routing strategy on a scale-free network is proposed,in which the node adjusts the forwarding probability with the dynamical traffic load(packet queue length) and the degree distribution of neighbouring nodes.The critical queue length of a node is set to be proportional to its degree,and the node with high degree has a larger critical queue length to store and forward more packets.When the queue length of a high degree node is shorter than its critical queue length,it has a higher probability to forward packets.After higher degree nodes are saturated(whose queue lengths are longer than their critical queue lengths),more packets will be delivered by the lower degree nodes around them.The adaptive local routing strategy increases the probability of a packet finding its destination quickly,and improves the transmission capacity on the scale-free network by reducing routing hops.The simulation results show that the transmission capacity of the adaptive local routing strategy is larger than that of three previous local routing strategies.