一种动态网格Overlay Network拓扑优化蚁群算法

如何提供丰富的通信交互是高级网格应用的关键问题，但是目前的网格技术还不能满足这一需求，提出了一种基于Overlay Network的网格架构来弥补这一不足，如何优化Overlay Network拓扑结构是该架构必须首先解决的重要问题，给出了一种动态环境中拉各朗日蚁群优化算法DLagrAnt，计算的结果显示该算法具有更快的适应性和更小的开销。     

用于网格计算的Overlay Network设计算法的研究*

针对如何提供丰富的通信交互提出了一种基于Overlay Network的网格架构,设计Overlay Network,给出了一种动态环境中分布式拉格朗日启发式算法DLagr OTDP,计算的结果显示该算法具有更快的适应性。     

SIP终端中拓扑感知的P2P中继查找机制实现①

由于Internet采用尽力而为的服务,VoIP系统存在QoS问题。为改善QoS,多数系统采用“应用层路由”方案,该方案关键是如何查找拓扑最优中继节点。结合P2P技术,提出一种拓扑感知的P2P中继查找机制。首先构建一个Cluster Overlay网络来模拟真实的Internet拓扑;在Cluster Overlay网络上设计一种拓扑感知的中继查找算法;最后在SIP终端中实现这种方案。在NS2平台上对Cluster Overlay网络和中继查找机制进行了仿真实验。结果表明：Cluster Overlay网络和真实的Internet拓扑相似度很高,中继查找算法能查找到最优中继结点,从而改进了路径质量。     

一种基于Mesh结构Overlay网络的构建算法

Overlay网络是一种构建在IP层网络之上的,由端系统之间的逻辑连接构成的应用层网络。因为Overlay网络易于构建、管理灵活和可扩展性强,在实现Internet上的多种应用中发挥了越来越重要的作用。但是,Overlay网络的连接延时长、抖动大,而且容易形成Overlay网络逻辑连接共享物理链路瓶颈的情况,这样的Overlay网络路由效率低而且鲁棒性差,很难保证诸如流媒体这样的实时应用的Qos要求。文章提出了一种基于Mesh结构的Overlay网络的构建算法。该算法运用了地标聚类和相关路径选择策略,目的是建立一个适应物理拓扑、适用于实时大流量业务的Overlay网络。算法规定当一个节点要加入Overlay网络时,通过测量地标使物理拓扑距离较近的节点相互连接,并且进一步地选择相对独立的多条连接,优化了邻居节点选择、Overlay网络的构建过程。实验结果表明本算法构建的Overlay网络在承载大流量业务时,仍可以保持较大的吞吐量和较好的网络可扩展性。     

基于Overlay Network的网格虚拟端到端带宽分配问题研究

提出了基于Overlay Network的服务网格（SGON）的概念，以此来生成和部署网络增值服务.带宽分配问题是在Overlay Grid上部署和运行增值服务的关键问题，为了解决这个问题，Overlay Grid在已存在的数据传输网络的上部建立了一个逻辑的端到端的服务传输基础，通过双边的服务水平协议（SLA），提供QoS约束的带宽.给出了包含SLA、服务QoS、流量需求分布、开销等因素的带宽分配问题的模型，同时提供了静态和动态带宽分配问题的分析模型和近似解，最后设计了一种启发式自适应在线动态带宽分配算法.实验仿真结果表明，该算法是可行的.     

主动Overlay物联网QoS服务路由算法研究

在物联网环境中,服务可能由任意多个结点汇聚而成,而传统的尽力而为的通信服务不能保证服务质量(QoS)。为此,首先提出了主动Overlay物联网服务路由逻辑拓扑结构,然后对物联网服务路由问题进行了建模。在此基础上,设计了基于Agent和蚁群优化(ACO)的主动Overlay物联网QoS蚁群服务路由算法。该算法结合移动A-gent对ACO进行了改进,实现了保证QoS的服务路径选择。最后从理论上证明了该算法的正确性和收敛性,同时通过仿真实验对该算法的实际性能进行了验证和比较。     

基于P2P的蠕虫防御模型

针对大规模网络环境下的蠕虫问题，本文提出一种面向蠕虫防御的层次化P2P重叠网模型（Hierachical Peer-to-Peer Overlay Network for Worm Prevention，简称HPOWP）。HPOWP通过层次式的P2P构架进一步提高了传统P2P系统的可缩放性，有效地与现有网络基础设施的拓扑结构相适应。在HPOWP模型中构造基于DHT的聚合树，提高蠕虫的识别效率。该模型为大规模复杂网络环境中的蠕虫防御提供了很好的解决方案。     

面向复杂拓扑的Relay查找机制

在应用层路由系统中,针对Internet的复杂拓扑问题,提出一种Cluster Overlay改进模型和拓扑感知的Relay查找机制。改进模型能根据复杂网络拓扑自适应地修正Cluster划分,使Cluster Overlay与Internet拓扑更接近。拓扑感知的Relay查找机制较好地利用了复杂拓扑现象,进一步改进了路径质量。实验结果显示,改进的Cluster Overlay模型和Relay查找机制具有较好的性能。     

基于Chord的结构化P2P路由改进算法

路由效率是结构化P2P覆盖网最关键的问题,结构化P2P网络是构建在于物理网络拓扑之上的一层Overlay网络.不考虑物理网络的拓扑结构,从而导致覆盖网与物理拓扑不匹配,导致了较大的网络延迟.提出了一个基于邻接表的路由改进算法,通过模拟仿真实验证明,该算法能在很大程度上解决不匹配问题,并达到提高路由效率的研究目的.     

计算机网络连接增强问题的模拟退火解决方法

对计算机网络的拓扑结构进行扩展时 ,经常要解决这样一类优化问题 :在给定预算限制的条件下 ,选择一组连接 ,使带来的利润最大。本文运用模拟退火算法 ,提出了一种解决方案 ,能较快地得到最优解。     

Dimensioning and on-line scheduling in Lambda Grids using divisible load concepts

Due to the large amounts of data required to be processed by the typical Grid job, it is conceivable that the use of optical transport networks in Grid deployment (hence the term “Lambda Grid”) will increase. The exact topology of the interconnecting network is obtained by solving a dimensioning problem, and the outcome of this strongly depends on both the expected workload characteristics and Grid scheduling policy. Solving this combined scheduling and dimensioning problem using straightforward ILP modelling is cumbersome; however, for steady-state Grid operation, Divisible Load Theory (DLT) can yield scalable formulations of this problem. In this paper, the on-line hierarchical scheduling on a lambda Grid of workload approaching the Grid’s capacity in a two-tier Grid mode of operation is studied. A number of these algorithms are goal-driven, in the sense that target per-resource goals are obtained from the off-line solution to the Divisible Load model. We compare these on-line multiresource scheduling policies for different workloads, Grid interconnection topologies and Grid parameters. We show that these algorithms perform well in the studied scenarios when compared to a fully centralized scheduling algorithm.

基于网格的遗传算法及其在公交运行计划编制中的应用研究

利用基于网格的遗传算法解决城市公共交通运营中的运行计划编制问题.首先应用有序样奉聚类算法对城市公交历史客流量样本数据进行数据挖掘,然后在综合考虑乘客待车成本和公交公司运营亏损等因素的前提下构造遗传算法的适应度甬数、编码方式和约束条件,最后在网格平台上初始化箅法种群,并分配不同的子种群到网格的各个集群、节点上并行地进行选择、交叉、变异及计算染色体的适应度等进化操作,同时以一定的规律在集群和集群、节点和节点之间交换优秀染色体,从而能快速得出满意的运行计划时刻表;通过仿真实验,证明了该方法的有效性和实时性.     

Distributed mutual exclusion algorithms for grid applications: A hierarchical approach

The majority of current distributed mutual exclusion algorithms are not suited for parallel or distributed applications on a Grid as they do not consider the heterogeneity of latency on Grids. We propose two distributed mutual exclusion algorithms, based on Naimi–Trehel's token-based algorithm, which take into account latency gaps, especially those between local and remote clusters of machines. Our first algorithm exploits cluster locality by giving higher priority to critical section requests issued from nodes of the same cluster when compared to those from remote nodes. Our second algorithm adds a router layer to the first algorithm, bringing it closer to Grid network topology. Viewing each cluster as a single node, the Naimi–Trehel algorithm is applied to this router layer. Redirection of inter-cluster messages to cluster's nodes is then minimized.     

Flexible Grid service management through resource partitioning

In this paper, a distributed and scalable Grid service management architecture is presented. The proposed architecture is capable of monitoring task submission behaviour and deriving Grid service class characteristics, for use in performing automated computational, storage and network resource-to-service partitioning. This partitioning of Grid resources amongst service classes (each service class is assigned exclusive usage of a distinct subset of the available Grid resources), along with the dynamic deployment of Grid management components dedicated and tuned to the requirements of a particular service class introduces the concept of Virtual Private Grids. We present two distinct algorithmic approaches for the resource partitioning problem, the first based on Divisible Load Theory (DLT) and the second built on Genetic Algorithms (GA). The advantages and drawbacks of each approach are discussed and their performance is evaluated on a sample Grid topology using NSGrid, an ns-2 based Grid simulator. Results show that the use of this Service Management Architecture in combination with the proposed algorithms improves computational and network resource efficiency, simplifies schedule making decisions, reduces the overall complexity of managing the Grid system, and at the same time improves Grid QoS support (with regard to job response times) by automatically assigning Grid resources to the different service classes prior to scheduling.     

军事情报网格传输路径优化算法研究

如何有效地传输情报数据是军事情报网格研究的重要问题。将蚁群网格路由算法应用于军事情报网格的传输路径选择,提出一种带删除算子的蚁群网格路由优化算法。在该算法中,通过加入一个称为删除算子的变量,在整个情报传输网格中引入优胜劣汰机制,进行强制淘汰,同时对组合参数进行选择进化,以实现组网路径的有效选择。通过仿真实验证明该算法明显提高情报组网传输性能。     

多智能体系统中基于局部测量信息的目标定位

针对多智能体系统中的定位问题,设计一种在变拓扑网络条件下的分布式目标定位算法,使所有智能体实现目标的相对定位.假设智能体只能获取局部的测量信息并进行局部信息交换.算法包括成对智能体之间相互定位算法和基于一致性的目标定位融合算法,对算法的成立条件和收敛误差进行分析,并通过仿真实验验证所提出算法的有效性.     

基于遗传算法的双向搜索Chord算法

针对传统Chord物理拓扑和逻辑拓扑不匹配以及空间复杂度导致搜索效率低的问题,结合遗传算法和双向搜索改进算法的优点,提出了一种基于遗传算法的双向搜索Chord算法,。该算法结合遗传算法,将拓扑匹配问题看成一个旅行商问题(TSP问题),利用遗传算法寻找此问题的最优解,然后用得出的最优解构建Chord环,解决物理拓扑和逻辑拓扑不匹配的问题,在此基础上,使用双向查找算法,进一步加快查找速度。实验结果表明,该算法在查询路径长度和查找跳数上比传统的Chord算法具有更高的性能。     

Distributed Zero-Gradient-Sum (ZGS) consensus optimisation over networks with time-varying topologies

This paper addresses a distributed consensus optimisation problem over networks with time-varying topologies based on Zero-Gradient-Sum (ZGS) algorithm. First, the exponential convergence of the algorithm is guaranteed under a new condition on network topologies, called cooperatively connected. This condition does not require the topology constantly connected or jointly connected but only requires the integral of the Laplacian matrix of the network topology over a period of time is connected. Hence, it is suitable for more general time-varying topologies. Second, by establishing a key mathematical lemma, we develop a convergence analysis technique which is based on the difference of the Lyapunov function rather than its differentiation. Finally, a simulation example is also provided to verify the results obtained in this paper.     

Building Efficient Overlays

Peer-to-peer (P2P) and Grid computing systems have emerged as popular models aiming at further utilizing Internet information and resources, complementing the available client–server services. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a serious topology mismatch problems between the overlay network and the physical underlying network. The topology mismatch problem brings a great stress in the Internet infrastructure and greatly limits the performance gain from various search or routing techniques in P2P and Grid systems. Aiming at alleviating the mismatch problem and reducing the unnecessary traffic, we have proposed two approaches, adaptive overlay topology optimization (AOTO) and location-aware topology matching (LTM) techniques, to reduce the total traffic cost and average query response time. Both AOTO and LTM are scalable and completely distributed in the sense that they do not require any global knowledge of the whole overlay network when each node is optimizing the organization of its logical neighbors. This paper shows the effectiveness of AOTO and LTM and compares the performance of these two approaches through simulation studies.     

具有模糊处理时间的网格任务调度免疫算法

目前，网格计算作为一种新的计算范式正在兴起。任务调度是其中的一个重要研究领域。该文以AIS的克隆选择算法为基础，给出了基于人工免疫系统的网格任务调度算法。首先，对网格任务调度问题进行模糊化，并给出了形式化描述，随后用结构化的语言对算法进行了说明，最后通过仿真实验对算法的有效性以及算法参数对性能的影响进行了验证。