基于双种群遗传算法的MPLS流量工程研究

针对因特网流量的快速增加和对服务质量要求的不断提高,为避免网络数据流过大造成拥塞问题,利用MPLS流量工程的路由算法成为业界研究的重点。介绍了MPLS流量工程的工作机制,并提出了数学模型。MPLS流量工程的关键问题是LSP的分布优化问题,针对这一问题的Np-hard特性,提出一种基于双种群策略的遗传算法求解方法,算法采用自然数编码来提高搜索效率。算法中,两个子种群周期性地交流最优染色体,进一步提高了性能。仿真实验表明,提出的算法能有效提高搜索速度,增加获得最优解的概率。     

基于粗粒度的路由切割调度及实现方法

基于N元非合作模型的路由切割调度算法,根据路由表空间串行流量调度的情况,帮助单控制节点上从多选择域中得到渐进次优解,算法搜索局部空间,有很好的收敛特性和较低的时间复杂性.使得IP地址空间从单控制节点映射到多条链路上时能够保持这些链路上的流量均衡,利用设计的单点解空间的切割调度算法SSA,找出链路e1的有限个路由分配的可行解.对基于粗粒度的策略路由通过路由切割调度的思想变为细粒度的任务调度进行了研究并给出一种利用NS2网络仿真试验和工程实现方法.     

服务覆盖网络中一种动态流量工程模型与算法

针对服务覆盖网络中的自私路由造成的网络流量失衡将严重影响网络效率和稳定性的问题, 研究如何在覆 盖层应用动态流量工程的方法进行流量优化分配. 基于随机动态优化配流理论, 提出一种服务覆盖网络的动态流量 工程模型, 并设计了分布式的流量工程算法. 该算法可以折衷控制路由的自私与负载均衡的程度. 模拟实验显示, 所 提出的方法较其他方法具有更好的性能, 尤其对于实时动态流量有着较好的适应性.

     

一种新型流量工程算法

提出一种流量工程算法,由基于时延、跳数和链路优先级的动态路由算法和负载均衡算法组成。这种新型算法不仅能够完成流量工程的任务,相对于其它算法,还具有收敛速度快,简单高效的特点。通过理论分析详尽说明算法的设计思想和相对于现有算法的优越性。     

基于NS2的MPLS流量工程仿真研究

MPLS被认为是下一代Internet骨干网络的核心技术,利用MPLS可以有效实施流量工程。文中对MPLS技术进行了介绍,针对流量工程的要求,给出了满足MPLS流量工程要求的系统模型。通过对仿真工具NS2的扩展,建立了一个MPLS流量工程仿真器,可以实现对MPLS转发技术和约束路由算法进行性能评估。仿真器的核心思想是,在标记交换路由器LSR模型中实现了标记分发、信令协议和路由协议扩展等基本功能;在标记边缘路由器LER模型上实现了对显示路由算法的支持。     

基于NS2的MPLS流量工程仿真研究

MPLS被认为是下-代Internet骨干网络的核心技术,利用MPLS可以有效实施流量工程.文中对MPLS技术进行了介绍,针对流量工程的要求,给出了满足MPLS流量工程要求的系统模型.通过对仿真工具NS2的扩展,建立了一个MPLS流量工程仿真器,可以实现对MPLS转发技术和约束路由算法进行性能评估.仿真器的核心思想是,在标记交换路由器LSR模型中实现了标记分发、信令协议和路由协议扩展等基本功能;在标记边缘路由器LER模型上实现了对显示路由算法的支持.     

多模态多目标差分进化算法求解非线性方程组

针对当前算法在求解非线性方程组时面临解的个数不完整、精确度不高、收敛速度慢等问题进行了研究,提出一种多模态多目标差分进化算法。首先将非线性方程组转换为多模态多目标优化问题,初始化一个随机种群并对种群中全部个体进行评价;然后通过非支配解排序和决策空间拥挤距离选择机制,挑选种群中的一半优质个体进行变异;接着在变异过程中采用一种新的变异策略和边界处理方法以增加解的多样性;最后通过交叉和选择机制使优质个体进行进化,直到搜索到全部最优解。在所选测试函数集和工程实例上的实验结果表明,该算法能有效地搜索到非线性方程组的解,并通过与当前四个算法进行比较,该算法在解的数量和成功率上具有优越性。     

一种基于带宽和时延的流量工程动态路由选择算法

在流量工程的路由选择模式中动态路由选择算法最重要。本文研究了多种动态路由选择算法后，提出了一种基于带宽和时延的流量工程动态路由选择算法，并通过实际仿真，验证了该算法的有效性和便利性。     

面向对象设计在捷联惯导算法仿真中的应用

针对捷联惯导系统（SINS）的算法设计,介绍了SINS的姿态、速度和位置基本微分方程组;以解捷联惯导一阶微分方程组的算法仿真为例,提出了基于面向对象程序设计中类的概念和运算符重载的功能,设计了捷联惯导算法的四阶龙格-库塔数值解法;该设计为工程实现中直接求解含标量、向量和矩阵等混合形式的一阶微分方程组的数值解提供了一定的参考价值;仿真结果表明：即使对恶劣的纯锥运动,该算法精度也很高。     

基于局部线性嵌入算法的流量矩阵流形结构分析

利用经典流形学习算法研究流量矩阵中的流形结构,能够获得流量矩阵的本征维度。然而局部线性嵌入(LLE)算法依赖于近邻点的选取,传统近邻选取个数往往通过实验试凑法得到最优解,不能完全揭示流量矩阵的流形结构。针对上述缺点,提出一种改进的局部线性嵌入算法,该算法利用改进的LLE算法探索流量矩阵的流形结构,并对实际骨干网络中的流量矩阵进行分析。实验结果证明,改进算法具有较小的重构误差,相对于标准LLE算法,能更为准确地揭示流量矩阵的低维流形结构。     

A genetic algorithm for finding a path subject to two constraints

Multi-constrained routing (MCR) aims to find the feasible path in the network that satisfies multiple independent constraints, it is usually used for routing multimedia traffic with quality-of-service (QoS) guarantees. It is well known that MCR is NP-complete. Heuristic and approximate algorithms for MCR are not effective in dynamic network environment for real-time applications when the state information of the network is out of date. This paper presents a genetic algorithm to solve the MCR problem subject to transmission delay and transmission success ratio. Three key design problems are investigated for this new algorithm, i.e., how to encode the problem in genetic representation, how to avoid the illegal chromosomes in the process of population initialization and genetic operation, and how to design effective genetic operator. We propose the gene structure (GS) to deal with the first problem, and the gene structure algorithm (GSA) to generate the GS. Based on the GS, we provide the heuristic chromosome initialization and mutation operator to solve the last two problems. Computer simulations show that the proposed GA exhibits much faster computation speed so as to satisfy the real-time requirement, and much higher rate of convergence than other algorithms. The results are relatively independent of problem types (network scales and topologies). Furthermore, simulation results show that the proposed GA is effective and efficient in dynamic network environment.     

A lexicographically fair allocation of discrete bandwidth for multirate multicast traffics

Fair bandwidth allocation is an important issue in the multicast network to serve each multicast traffic at a fair rate commensurate with the receiver's capabilities and the capacity of the path of the traffic. Lexicographically fair bandwidth layer allocation problem is considered and formulated as a nonlinear integer programming problem. A nonincreasing convex function of the bandwidth layers of the virtual sessions is employed to maximize the bandwidth of each virtual session from the smallest.To solve the fairness problem a genetic algorithm (GA) is developed based on the fitness function, ranking selection and the shift crossover. Outstanding performance is obtained by the proposed GA in various multicast networks. The effectiveness of the GA becomes more powerful as the network size increases.     

Network-aware virtual machine migration in an overcommitted cloud

Virtualization, which acts as the underlying technology for cloud computing, enables large amounts of third-party applications to be packed into virtual machines (VMs). VM migration enables servers to be reconsolidated or reshuffled to reduce the operational costs of data centers. The network traffic costs for VM migration currently attract limited attention.However, traffic and bandwidth demands among VMs in a data center account for considerable total traffic. VM migration also causes additional data transfer overhead, which would also increase the network cost of the data center.This study considers a network-aware VM migration (NetVMM) problem in an overcommitted cloud and formulates it into a non-deterministic polynomial time-complete problem. This study aims to minimize network traffic costs by considering the inherent dependencies among VMs that comprise a multi-tier application and the underlying topology of physical machines and to ensure a good trade-off between network communication and VM migration costs.The mechanism that the swarm intelligence algorithm aims to find is an approximate optimal solution through repeated iterations to make it a good solution for the VM migration problem. In this study, genetic algorithm (GA) and artificial bee colony (ABC) are adopted and changed to suit the VM migration problem to minimize the network cost. Experimental results show that GA has low network costs when VM instances are small. However, when the problem size increases, ABC is advantageous to GA. The running time of ABC is also nearly half than that of GA. To the best of our knowledge, we are the first to use ABC to solve the NetVMM problem.     

一种基于MPLS流量工程的QoS保证路由算法

随着因特网流量的快速增加和对服务质量要求的不断提高,基于MPLS流量工程的QoS路由算法成为业界研究的重点。文章介绍了MPLS流量工程的工作机制和原理,在传统的Dijkstra算法的基础之上,提出了一种新颖的基于遗传优化的QoS保证的路由选择算法,同时配置一批LSP,并克服了一条一条配置时的先占先优的“顶端优势”。最后的仿真实验说明了算法在优化网络资源、平衡网络负载中是有效的和实用的。     

采用改进LM算法的网络流量分类方法

针对传统的流量分类方法准确率低、开销大、应用范围受限等问题,提出一种有效的网络流量分类方法（GA-LM）。该方法将基于神经网络的分类方法作为网络流量的分类模型,采用L-M算法构造分类器,并用遗传算法优化网络初始连接权值,加速了网络收敛过程,提高了分类性能。通过对收集到的实际网络流量数据进行分类,实验结果表明GA-LM比标准BP算法和L-M算法的收敛速度快,具有较好的可行性和高准确性,从而可有效地用于网络流量分类中。     

基于深度学习的网络流时空特征自动提取方法

流量异常检测是网络入侵检测的主要途径之一,也是网络安全领域的一个热门研究方向。通过对网络流量进行实时监控,可及时有效地对网络异常进行预警。目前,网络流量异常检测方法主要分为基于规则和基于特征工程的方法,但现有方法需针对网络流量特征的变化需重新人工收集规则或 构造特征,工作量大且繁杂。为解决上述问题,该文提出一种基于卷积神经网络和循环神经网络的深度学习方法来自动提取网络流量的时空特征,可同时提取不同数据包之间的时序特征和同一数据包内字节流的空间特征,并减少了大量的人工工作。在 MAWILab 网络轨迹数据集上进行的验证分析结果表明,该文所提出的网络流时空特征提取方法优于已有的深度表示学习方法。     

Optimisation of control parameters for genetic algorithms to test computer networks under realistic traffic loads

Although many studies have focused on testing computer networks under realistic traffic loads by means of genetic algorithms (GAs), little attention has been paid to optimising the parameters of the GAs in this problem. The objective of this work is to design and validate a system that, given some constraints on traffic bandwidth, generates the worst-case traffic for a given computer network and finds the traffic configuration (critical background traffic) that minimises throughput in that computer network. The proposed system is based on a meta-GA, which is combined with an adaptation strategy that finds the optimum values for the GA control parameters and adjusts them to improve the GA's performance. To validate the approach, different comparisons are performed with the goal of assessing the acceptable optimisation power of the proposed system. Moreover, a statistical analysis was conducted to ascertain whether differences between the proposed system and other algorithms are significant. The results demonstrate the effectiveness of the system and prove that, when the background traffic is driven by a GA that uses the parameters obtained from the system, the computer network's performance is much lower than when the traffic is generated by Poisson statistical processes or by other algorithms. This system has identified the worst traffic pattern for the protocol under analysis.     

基于遗传和禁忌搜索混合算法的预制生产调度的研究

遗传算法是一种全局搜索能力较强的元启发式算法,可通过不断进化种群得到最优或近优解;但是遗传算法的局部搜索能力较差,容易发生早熟收敛问题。因此为了克服遗传算法早熟收敛的问题,考虑到禁忌搜索算法的局部搜索能力较强的优势,提出了一种遗传和禁忌搜索的混合算法解决预制生产流水车间的提前和拖期惩罚问题。该混合算法是在遗传算法每次迭代后,通过禁忌搜索改进当前种群中的最好染色体,并替换种群中适应度值最差的染色体。经实验测试表明,所提出的混合算法的性能更优,更容易得到全局最优解或近优解。     

Genetic algorithm based on receding horizon control for arrival sequencing and scheduling

The concept of Receding Horizon Control (RHC) is introduced into Genetic Algorithm (GA) in this paper to solve the problem of arrival scheduling and sequencing (ASS) at a busy hub airport. A GA-based method is proposed for solving the dynamic ASS problem, and the focus is put on the methodology of integrating the RHC strategy into the GA for real-time implementations in a dynamic environment of air traffic control. Receding horizon and terminal penalty are investigated in depth as two key techniques of this novel RHC-based GA. Simulation results show that the new method proposed in this paper is effective and efficient to solve the ASS problem in a dynamic environment.     

基于MPLS的下一代网络QoS模型

介绍集成服务、区分服务、流量工程和MPLS几种常用的服务质量(QoS)技术,任何一种独立的技术都不能很好地解决QoS问题。该文提出一种将流量工程引入多协议标签交换(MPLS)网络并结合DiffServ和IntServ的QoS模型,阐述其原理及部分实现技术,利用NS2工具对提出的网络模型进行仿真。结果表明,与单纯的MPLS网络相比,该模型改善了网络延时,丢包率降低20%。