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     

无标度复杂网络负载传输优化策略

提出了一种能够显著提高无标度复杂网络负载传输性能的优化路由策略.实现了负载在核心节点与边缘节点间的合理分配.分析表明该策略使得网络的负载处理能力正比于网络规模的平方,而与单个节点的度值无关.实验结果显示优化路由策略在保持了最短路由策略小世界效应的同时,成倍地提升了网络的负载传输能力,且随着网络平均节点度的增加其优势越趋显著.此外,与有效路由策略的比较进一步验证了优化路由策略的优异性能. 关键词： 优化路由策略 复杂网络 负载传输 网络阻塞     

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.     

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.     

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     

RWA方法对WDM网络P圈优化性能的影响

研究不同的路由和波长分配（RWA）方法对无波长变换WDM网络P圈优化性能的影响.提出了用负载均衡的方法对各波长层的工作容量进行均衡,以降低网络总容量.分别研究了动态分层通用RWA（DL-GRWA）、最短路径RWA（SP-RWA）、动态分层负载均衡（DL-LB）、最短路径负载均衡（SP-LB）、固定波长负载均衡（FW-LB）5种方法对网络总容量的影响.仿真发现,无论何种RWA方法,随着圈最大跳数限制的变大,网络总容量都逐渐降低,其中SP-LB方法所需要的网络总容量最小.     

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     

基于蚁群优化的IP over WDM光网络动态生存性映射算法

随着光网络规模的不断扩大以及网络体系结构的扁平化过程加速,网络传输的可靠性和实时性以及生存性路面算法本身面临新的挑战本文在单点割集的松弛生存性约束条件下,结合动态业务下的IP over WDM光网络的特点.借助蚁群优化算法的强鲁棒性和记忆能力,通过改变与调整蚁群优化算法结构中的信息素结构与其更新机制,在动态路由选择过程中,引入网络可生存性约束信息改进路由选择概率计算机制,来实现IP over WDM光网络动态生存性映射的路由策略,与其他同类算法相比,该算法不再存储网络备用路由集和物理拓扑自身庞大的割集关系,在保证网络生存性效果的前提下,通过割集松弛条件有效降低了计算的时间复杂度,提高算法收敛速度,适应网络实时性要求,仿真结果表明:与传统最短路算法比较,该算法能有效提高动态光网络的可生存性映射性能和网络的资源利用效率,并降低业务阻塞率.     

基于蚁群优化的IP over WDM光网络动态生存性映射算法

随着光网络规模的不断扩大以及网络体系结构的扁平化过程加速,网络传输的可靠性和实时性以及生存性路面算法本身面临新的挑战.本文在单点割集的松弛生存性约束条件下,结合动态业务下的IP over WDM光网络的特点,借助蚁群优化算法的强鲁棒性和记忆能力,通过改变与调整蚁群优化算法结构中的信息素结构与其更新机制,在动态路由选择过程中,引入网络可生存性约束信息改进路由选择概率计算机制,来实现IP over WDM光网络动态生存性映射的路由策略.与其他同类算法相比,该算法不再存储网络备用路由集和物理拓扑自身庞大的割集关系,在保证网络生存性效果的前提下,通过割集松弛条件有效降低了计算的时间复杂度,提高算法收敛速度,适应网络实时性要求.仿真结果表明:与传统最短路算法比较,该算法能有效提高动态光网络的可生存性映射性能和网络的资源利用效率,并降低业务阻塞率.     

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.     

The information transmission in community networks

The community structure has been empirically found in many real networks. This paper proposes an efficient Double Shortest Path routing strategy trying to avoid the modules of traffic congestion, which means that we adopt the shortest routing strategy both in the inter-modules and in the intra-module. Simulations show that this routing algorithm is superior to the traditional shortest path routing protocol with appropriate selection of the tunable parameters. In addition, this algorithm can also be improved by integrating it with several alternative routing strategies.     

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.     

Effective strategy of adding nodes and links for maximizing the traffic capacity of scale-free network

In this paper, we propose an efficient strategy to enhance traffic capacity via the process of nodes and links increment. We show that by adding shortcut links to the existing networks, packets are avoided flowing through hub nodes. We investigate the performances of our proposed strategy under the shortest path routing strategy and the local routing strategy. Our obtained results show that using the proposed strategy, the traffic capacity can be effectively enhanced under the shortest path routing strategy. Under the local routing strategy, the obtained results show that the proposed strategy is efficient only when packets are more likely to be forwarded to low-degree nodes in their routing paths. Compared with other strategies, the obtained results indicate that our proposed strategy of adding nodes and links is the most effective in enhancing the traffic capacity, i.e., the traffic capacity can be maximally enhanced with the least number of additional nodes and links.     

A packet routing strategy using neural networks on scale-free networks

We propose a dynamic packet routing strategy by using neural networks on scale-free networks. In this strategy, in order to determine the nodes to which the packets should be transmitted, we use path lengths to the destinations of the packets, and adjust the connection weights of the neural networks attached to the nodes from local information and the path lengths. The performances of this strategy on scale-free networks which have the same degree distribution and different degree correlations are compared to one another. Our numerical simulations confirm that this routing strategy is more effective than the shortest path based strategy on scale-free networks with any degree correlations and that the performance of our strategy on assortative scale-free networks is better than that on disassortative and uncorrelated scale-free networks.     

Traffic dynamics in scale-free networks with tunable strength of community structure

In this paper we systematically investigate the impact of community structure on traffic dynamics in scale-free networks based on local routing strategy. A growth model is introduced to construct scale-free networks with tunable strength of community structure, and a packet routing strategy with a parameter α is used to deal with the navigation and transportation of packets simultaneously. Simulations show that the maximal network capacity stands at α=−1 in the case of identical vertex capacity and monotonously decreases with the strength of community structure which suggests that the networks with fuzzy community structure (i.e., community strength is weak) are more efficient in delivering packets than those with pronounced community structure. To explain these results, the distribution of packets of each vertex is carefully studied. Our results indicate that the moderate strength of community structure is more convenient for the information transfer of real complex systems.     

Efficient and robust routing on scale-free networks

Information routing is one of the most important problems in large communication networks. In this paper we propose a novel routing strategy in which the optimal paths between all pairs of nodes are chosen according to a cost function that incorporates degrees of nodes in paths. Results on large scale-free networks demonstrate that our routing strategy is more efficient than the shortest path algorithm and the efficient routing strategy proposed by Yan et al. [Phys. Rev. E 73, 046108 (2006)]. Furthermore our routing strategy has strong robustness against cascading failure attacks on networks.     

Traffic dynamics based on a traffic awareness routing strategy on scale-free networks

By incorporating local traffic information into the shortest path routing strategy, we numerically investigate the effectiveness of the traffic awareness routing strategy for scale-free networks with different clustering. In order to characterize the efficiency of the packet-delivery process, we introduce an order parameter and an average transmission time that allow us to measure the network capacity by the critical value of phase transition from free flow to congestion. Compared with the shortest path routing protocol, the network capacity is greatly enhanced by the traffic awareness routing strategy. We also find that there exists an optimum value for the tunable parameter in the congestion awareness strategy. Moreover, simulation results show that the more clustered the network, the less efficient the packet-delivery process.