A combinatorial approximation algorithm for concurrent flow problem and its application

The multi-commodity flow problem involves simultaneous shipping several different commodities from sources to sinks in a directed network with total amount of flow going through an edge limited by its capacity. The optimization version of the multi-commodity flow problem is the maximum concurrent flow problem, which finds a flow with the minimum congestion. For any positive ε, the ε-optimal concurrent flow problem is to find a solution whose the congestion value is no more than (1+ε) times the minimum congestion. In recent years, a few fast combinatorial approximation algorithms for the ε-optimal concurrent flow problem have been presented. In this paper we propose a new variant of the combinatorial approximation algorithm: CACF with a tighter computation bound in decreasing the values of congestion and the potential function. Numerical comparisons are made between the results obtained by the combinatorial approximation algorithms and those did by the linear programming package CPLEX on large-scale test networks. The application of CACF to efficiently solving the system-optimal network flow problem is given where good results have been obtained. It has shown the capacity of the CACF in dealing with problems of concurrent flow associated.     

基于混合策略的QoS选播流路由优化算法

分析了QoS选播流交错服务问题的产生过程及其对网络资源的浪费，提出了一种基于混合策略的全局路由优化解决方法．在构建路由优化问题模型的基础上，得出了选播流路由端到端时延、服务器负载、网络流量、路径调整等多个优化目标和QoS约束的表达式．给出了GA、SA、TS三种算法有机结合形成的一种混合优化算法，并说明了运用该算法求解的关键步骤和实现过程．实验结果表明，本算法能够在满足QoS约束的前提下实现选播路由的多目标组合优化，与基于GA或者SA的求解算法相比具有更强的稳定性和更高的精确度．     

NGI中一种混合智能QoS组播路由算法

针对满足多个约束条件的服务质量（Quality of Service,QoS）组播路由的特点,提出了一种下一代互联网（Nem Generation Internet,NGI）中基于粒子群优化（Particle Swarm Optimization,PSO）和遗传算法（Genetic Algorithm,GA）的混合智能QoS组播路由算法。给出了QoS组播路由问题模型及其数学描述,结合PSO的快速搜索和GA的全局寻优能力,解决了多约束QoS组播路由问题,并在多个实际的和虚拟的网络拓扑上进行了仿真实现与性能评价。仿真结果表明,本文提出的算法是可行和有效的。     

Modeling the Internet congestion control using a Smith controller with input shaping

The Internet has shown a great capability of endless growing without incurring congestion collapse. The key of this success lies in its TCP/IP congestion control algorithm. In this paper, we use control theoretic analysis to model the Internet flow and congestion control as a time delay system. We show that the self-clocking principle, which is known to be a key component of any stable congestion Internet control algorithm, corresponds to implement a simple proportional controller (P) plus a Smith predictor (SP), which overcomes feedback delays that are due to propagation times. Different variants of TCP congestion control algorithms, such as classic TCP Reno or the recent Westwood TCP, can be modeled in a unified framework by proper input shaping of the P+SP controller structure. Finally, we show that controllers that do not implement the Smith predictor, such as proportional (P) controllers or proportional+derivative+integral (PID) controllers, provide an unacceptable sluggish system because they do not implement dead-time compensation.     

WSN中基于蚁群算法的QoS路由协议

蚁群算法的随机自适应性使得它很适合应用于无线传感器网络( WSN)环境中,所以本文针对WSN中的数据拥塞、传输延迟、能量消耗等问题,提出了一种基于蚁群算法的QoS路由协议.协议将如何搜索最佳路径问题抽象为组合规划问题,根据最小费用流规则定义了高带宽和低时延路径的判决条件,利用蚁群优化算法,寻找到不同目标函数的路径,达到...     

LTE系统中提高TCP性能的资源调度算法研究

LTE可以提供真正无处不在基于IP的移动宽带业务,但随着承载网的IP化,网络拥塞、丢包、抖动、延时等质量问题将影响到LTE业务层的QoS质量.作为LTE无线资源管理的核心,研究并设计一个良好的资源调度算法是提高数据业务的性能和终端用户的体验是一个亟待解决的重要任务.本文通过借鉴LTE对VoIP数据分组的半持续调度算法的思想,提出了一种LTE的无线资源调度的改进方案.方案将TCP确认包映射到具有更高优先级的空闲逻辑信道,从而降低了ACK包在无线信道中发生丢弃和拥塞的概率,避免了TCP拥塞控制机制的频繁开启.仿真结果表明,本文提出的TCP确认包映射转换方案在RTT时延、吞吐量等方面均有一定的提升,具有一定的稳定性和性能优势.     

一种端到端网络的不相交多路径QoS路由算法

不相交多路径路由算法旨在一个端到端的网络中为应用流选择多个路径，且这些路径在瓶颈链路上是彼此不相交的。本文提出的不相交多路径QoS路由（DMQR）算法在Dijkstra最短路径和最短最宽路径（SWP）算法的基础上，能够动态地计算时延最短、带宽最宽、在瓶颈链路上互不相交的路径，且保证每个路径都是满足一定服务质量的。在视频会议、远程医疗和远程教育等重要的视频通信场合，要求应用层和网络层必须协同工作以保证一些必要的QoS，例如端到端的带宽、时延和包丢失率等。本文针对端到端的网络，重点讨论不相交多路径QoS路由算法在应用层的设计和实现。性能分析和模拟结果显示，所提出的不相交多路径QoS路由算法总是收敛的，且当网络流量增加时，该算法具有较低的包丢失率和较高的吞吐量。     

Greedy distributed optimization of multi-commodity flows

The multi-commodity flow problem is a classical combinatorial optimization problem that addresses a number of practically important issues of congestion and bandwidth management in connection-oriented network architectures. We consider solutions for distributed multi-commodity flow problems, which are solved by multiple agents operating in a cooperative but uncoordinated manner. We provide the first stateless greedy distributed algorithm for the concurrent multi-commodity flow problem with poly-logarithmic convergence. More precisely, our algorithm achieves ${1+\varepsilon}The multi-commodity flow problem is a classical combinatorial optimization problem that addresses a number of practically important issues of congestion and bandwidth management in connection-oriented network architectures. We consider solutions for distributed multi-commodity flow problems, which are solved by multiple agents operating in a cooperative but uncoordinated manner. We provide the first stateless greedy distributed algorithm for the concurrent multi-commodity flow problem with poly-logarithmic convergence. More precisely, our algorithm achieves 1+e{1+\varepsilon} approximation, with running time O(H· log^O(1)m· (1/e)^O(1)){O(H{\cdot} \log^{O(1)}m{\cdot} (1{/}\varepsilon)^{O(1)})} where H is the number of edges on any allowed flow-path. No prior results exist for our model. Our algorithm is a reasonable alternative to existing polynomial sequential approximation algorithms, such as Garg–K?nemann (Proceedings of the 39th Annual Symposium on Foundations of Computer Science, Palo Alto, CA, USA, pp. 300–309, 1998). The algorithm is simple and can be easily implemented or taught in a classroom. Remarkably, our algorithm requires that the increase in the flow rate on a link is more aggressive than the decrease in the rate. Essentially all of the existing flow-control heuristics are variations of TCP, which uses a conservative cap on the increase (e.g., additive), and a rather liberal cap on the decrease (e.g., multiplicative). In contrast, our algorithm requires the increase to be multiplicative, and that this increase is dramatically more aggressive than the decrease.     

MHWSN中保证混合业务QoS的跨层调度算法

针对中高速传感器网络中混合业务QoS（Quality of Service）要求,跨层考虑物理层和数据链路层参数,提出了一种保证混合业务服务质量的调度算法AM-LWDF。该算法同时考虑时延优先级和吞吐量优先级,在满足实时业务QoS约束的前提下,以最大化系统吞吐量为目标建立了相应的优化模型,对实时业务能够满足时延较小的要求,对非实时业务满足吞吐量较大的要求。仿真结果表明,该调度算法可以灵活地在时延和吞吐量之间取得满意的折衷,并保证不同类型业务用户间的公平性。     

基于累积时延的流媒体传输模糊拥塞控制

为保证网络流媒体传输质量,在流媒体的传输中需要采用有效的拥塞控制策略.结合流媒体数据对时延敏感的特点,提出了一种基于累积时延的模糊拥塞控制算法,该算法在流媒体数据流传输过程中检测和跟踪其时延,在转发分组数据前,根据容忍时延阈值,丢弃超时数据包,减少不必要的带宽浪费,并且对所到达的数据流按照累积时延进行优先级分类,把全局性缓冲区和各队列的局部性缓冲区按照正常、拥塞避免和拥塞的规则划分为3个具有交叉过渡域的阶段,然后采用整体和局部相结合的拥塞控制方法,实现队列调度过程中的模糊处理,从而对网络拥塞进行有效的控制.理论分析和实验结果表明,使用基于累积时延的模糊拥塞控制算法,能有效改善流媒体的传输性能,是解决流媒体传输拥塞控制的有效途径,并能对提高网络性能起到重要作用.     

基于禁忌遗传优化的网络拥塞控制算法

为解决网络拥塞问题,提出一种基于禁忌遗传优化的网络拥塞控制算法TSGA。该算法将禁忌搜索和遗传算法相结合,以网络资源消耗最小化和网络负载均衡分布为目标,建立多约束QoS路由优化数学模型。仿真实验结果证明,该算法可实现网络拥塞控制,有效提高网络性能。     

A distributed channel assignment control for QoS support in mobile ad hoc networks

Recently, one of the most critical issues in mobile ad hoc networks (MANETs) is providing quality of service (QoS) through routing, access/admission control, resource reservation, and mobility management. However, most existing solutions do not provide QoS effectively due to the interference arising from mobility. In this paper, we refer to interference as a quasi-exposed node problem. To solve this problem, a new algorithm, named a distributed channel assignment control, is proposed that focuses on performance enhancements related to QoS and mathematical analysis techniques for the channel bandwidth. This novel algorithm uses channel assignment control with a power control to reduce the negative effects induced by the quasi-exposed node problem, and then the channels are adaptively negotiated to allow communication in the interference region. The proposed algorithm has been evaluated via extensive simulations, and the results show that it can successfully guarantee QoS by maintaining good throughput, reducing control message overhead, and enhancing delay.     

基于改进遗传算法的QoS路由选择优化

提出了一种基于改进遗传算法的QoS路由选择优化算法,采用网络资源消耗和负载分布为目标函数,凫目标函数包含了带宽、跳数、费用、时延等QoS参数。该算法在消耗网络资源最小的基础上,使负载均衡分布,合理利用了网络资源,降低了网络的拥塞,提高了网络的吞吐量。仿真计算的结果表明,该算法在解决多目标规划问题上是一个有效、可行的方法。     

一种优化网络资源利用的QoS路由选择的遗传算法

本文提出了一种优化网络资源利用的QoS路由选择的遗传算法，该方法在考虑网络带宽，时延的基础上，将资源消耗函数和网络负载分布作为目标函数，并利用遗传算法求最优解，希望所选路径消耗较少的网络资源，并使负载尽量均衡分布，达到合理利用网络资源，降低网络拥塞的目的，最后，给出了仿真实验结果。     

基于遗传算法的QoS路由优化算法

研究了带宽、时延等QoS路由问题,提出了一种基于遗传算法的QoS路由选择优化算法。算法采用网络资源消耗和负载分布为目标函数,目的是在消耗网络资源最小的基础上,使负载均衡分布,合理利用网络资源,降低网络拥塞。仿真结果表明,该算法是有效的、稳定的。     

ABR流量控制中的变结构控制器

自适应比特(available bit rate,简称ABR)业务的流量控制是ATM网络中一种有效的拥塞控制机制和流量管理手段.在高速的ATM网络中,算法的简洁性在很大程度上决定着交换机的性能.尽管二进制ABR流量控制的简洁性具有相当大的吸引力,但标准的EFCI算法控制的队列长度和允许信元速率(allowed cell rate,简称ACR)却容易出现大幅振荡的现象,这势必会降低链路的利用率,严重影响交换机的性能.进而又有了相对复杂却有效的显式速率反馈机制.在此研究中,以已有的ABR流量控制模型为基础,应用概率拥塞判定机制,并借助鲁棒控制理论中滑模变结构控制器的设计方法,为ABR流量控制设计了一种新的二进制算法,避免了标准EFCI算法中非线性环节诱发的自激振荡,这对于充分发挥二进制流控算法的简洁性以及优化交换机的性能是极为有利的.仿真实验表明:二进制流量控制中的滑模变结构算法大幅度地抑制了ACR和队列的振荡,平滑了由此而引入的时延抖动,为实现ATM网络中的服务质量提供了可靠的实现机制.     

Multi-channel live streaming in service overlay network

Service overlay network (SON) provides an effective means to deploy quality of service (QoS)-guaranteed live streaming over today’s Internet. A major challenge in designing such a network is dealing with resource sharing among multiple channels. To achieve the best overall QoS in SON, we devise a new multi-channel live streaming scheme. First, we propose a multi-tree construction algorithm by infrastructure-based overlay multicast. The algorithm employs pre-allocated session degree constraints in overlay nodes to reserve resources for multiple channels, and constructs multiple trees by considering the total resource utilization of overlay nodes. Second, we propose a tree-aware queue scheduling algorithm to reduce the overlay processing delay in view of the entire overlay network. Scheduling priority is identified to trade off session priority with node location in different trees. From simulation and experimental results, the scheme achieves a differentiated control among different sessions, provides load balancing among overlay nodes, and improves the delay performance on SON.     

改进人口迁移算法用于QoS路由优化

研究了带宽、时延等QoS路由问题,针对原人口迁移算法易收敛于局部最优且计算量大,计算时间长的特点,提出了一种改进的人口迁移算法（modified Population Migration Algorithm）,并将其应用于QoS路由选择优化问题。算法采用网络资源消耗和负载分布为目标函数,目的是在消耗网络资源最小的基础上,使负载均衡分布,合理利用网络资源,降低网络拥塞。仿真结果表明,该算法是有效的、稳定的。     

基于混合遗传算法的QoS多播路由算法

具有多QoS约束的多播路由问题具有NP完全的复杂度。基于延时、延时抖动、带宽、丢包率等QoS约束,描述了一种适应于研究QoS多播路由的网络模型,提出了基于遗传算法和禁忌搜索混合策略的具有多QoS约束的多播路由算法。该算法充分利用了遗传算法和禁忌搜索的优点。克服了遗传算法在求解多QoS约束多播路由问题中的爬山能力差以及不成熟收敛等问题。仿真实验结果表明,该算法为多QoS约束多播路由问题的求解提供了一种有效的新途径。     

一种基于遗传算法的多约束QoS多播路由优化算法

随着Internet、移动网络和高性能网络的不断发展,在网络和不确定参数下具有多约束QoS多播路由优化技术已成为网络及分布式系统领域的一个重要研究课题,这也是下一代Internet和高性能网络的难题。它吸引了许多爱好者。论文研讨了具有多约束QoS多播路由问题,其中主要包含延迟、延迟抖动、带宽、分组丢失率等QoS约束,文中描述了一种适应于研究QoS多播路由的网络模型。论文在网络环境及不确定参数下,提出了一种在网络规模、可行性方面优化Internet、移动网络和其他高性能网络的基于遗传算法的多约束QoS多播路由优化算法(MQROGA)。MQROGA在网络环境及不确定参数下能够优化网络资源,如带宽、延迟、分组丢失率等得到一个最优解和次优解。仿真实验结果表明,MQROGA为QoS多播路由提供了一种新的有效途径。