基于网络演算计算保证服务端到端延迟上界

归纳总结了网络演算,阐明了网络演算的两个基本工具——进入曲线和服务曲线,得出了服务曲线存在瓶颈效应、端到端延迟的理想与近似确定性上界、提供保证服务网络节点的服务曲线需求等结论,计算了服务曲线以速率等待时间及PGPS(packetizedgeneralizedprocessorsharing)形式表示的保证服务端到端延迟确定性上界.     

基于Internet控制系统延迟的一种解决方法

介绍了基于Internet的网络控制系统的组成及最近的研究状态，对于系统中网络时延这个重要问题进行了分析，通过分析提出了一种针对延迟的动态重发机制的解决方法，在被控端进行延迟时刻规律的完整学习，并且把此规律传给控制端。控制端依照此规律，在延迟发生概率较大的时刻进行瞬时重发，并不断得进行周期性得学习。从而降低了网络延迟情况．     

多提供商网络环境中移动代理路径寻优

在多提供商网络环境中,端用户在访问内容提供商提供的内容时,在一定的QoS约束下,服务提供商需要对多个网络提供商的网络性能和价格进行比较,为端用户确定一个最优路径.基于移动代理进行网络信息的采集,将网络延迟和成本均设为随机变量,在随机网络中建立了一个费用成本和延迟时间双重期望值目标的最小化模型,应用遗传算法对移动代理从服务提供商到内容提供商的路径进行了最优化求解.仿真结果表明了算法的有效性.     

一种路径干扰优化的多径距离矢量路由协议

多径路由协议可以同时使用多条路径传输数据,减少端到端延迟并提高吞吐量。然而,多条路径之间的干扰作用可能会对网络的整体性能造成影响,使网络无法实现预期的性能提升。提出了一种新的解决路径间干扰问题的算法IO-AOMDV(Interference-OptimizedAOMDV)。该方法引入路径相关度的计算来评估路径间干扰的大小,在路由发现过程中发现并建立路径相关度最小的路径对,然后使用该路径对传输数据。仿真结果表明,IO-AOMDV在包投递率、吞吐量、端到端平均延迟三个指标上可以得到较好的网络性能。     

基于Internet的网络控制系统端到端时延分析

端到端时延是影响基于Internet网络控制系统(NCS)稳定性的一个重要因素。该文通过OPNET建模仿真，分析了系统的端到端时延特征、时延组成及时延瓶颈。讨论了不同的数据包大小对端到端时延的影响及其原因，把该时延用于一个NCS仿真实例，分析了其对系统稳定性的影响。在NCS的设计过程中，明确端到端时延特征有助于提高系统的稳定性。     

Internet中的包延迟分布与包丢失关系研究

包延迟的分布特征是Internet端到端行为特征的一个重要组成部分，前人已在该方面进行了大量研究，但许多研究结论只适用于丢包率较小的情况，通过研究我国科技网和教育网上34条端到端路径上的延迟特征，得出了如下结论：（1）包延迟的分布特征与丢包率存在一定的关系；（2）在丢包率较小的情况下，包延迟的分布多具有单峰性，但随着丢包率的增大，包延迟的分布不再具有单峰性，而是呈现出越来越分散的特点；（3）随着丢包率的增大，固有延迟的发生次数呈现逐渐减少的趋势，当丢包率增大到一定程度，固有延迟的发生次数就很少了。     

一种IP与ATM网络基于多服务质量约束的路由算法

1 引言随着多媒体技术的飞速发展,网络上诸如数字视频和音频的各种多媒体应用通常都有严格的服务质量(QoS)要求。网络为了提供性能保证,只有采用资源预留和实行网络控制。近年来在ATM与Internet上的QoS要求己受到人们的极大重视。传统的数据传输网中路由选择主要与连通性有关。各种路由协议通常用诸如节点计数或延迟等单一的度量(metric)来表示网络的特性,并用最短路径算法来进行路由计算。为了支持广泛的QoS要求,这些路由协议需要有更复杂的模型,用诸如开销(cost)、延迟、延迟变量、丢失概率和带宽等多度量来表示网络的特性。QoS路由寻址的基本问题是找一条满足一种或多种QoS约束条件、具有最小开销(或者最短距离)的     

一种适用于移动Adhoc网络的自适应多跳路由协议

跨层优化多径路由协议以跨层优化为手段，使用物理层，MAC层和网络层的信息做路由决策并在多条路径上同时传递数据包。实验表明，该协议拥有以往的多径路由协议在网络吞吐量和端到端延迟的优势，而且延长网络寿命并降低网络中传输比特信息所需消耗的能量。     

认知无线Mesh网络信道接入与路由协议研究*

针对认知用户可用频谱动态变化,数据链路由于无可用的授权信道而无法建立,导致路径中断的问题,引入了具有拥塞感知能力的多径路由协议。认知用户可通过在多条路径中选择具有SOP交集的下一跳节点进行通信,降低主用户在授权信道上出现对认知用户数据传输带来的干扰;并在路由协议中采用了拥塞控制技术,通过对节点的拥塞情况进行感知,可有效避免瓶颈节点的出现。仿真结果表明,该协议在认知无线Mesh网络中,能较好地减小端到端延迟,提高数据包的成功投递率,增加网络的整体吞吐量。     

基于跨层设计的无线传感器网络MAC协议

针对占空比MAC协议存在端到端传输延迟问题,提出一种新的占空比MAC协议——PRMAC。PRMAC通过跨层路由信息帧的提前传送可以调度数据包在一个周期内多跳传输,从而降低网络延迟,提高能量有效性。NS-2仿真结果表明,PRMAC在没有牺牲能量有效性的情况下,改进了传统占空比协议的端到端传输延迟,并能提高网络吞吐量。     

Analysis of large scale traceroute datasets in Internet routing overlays by parallel computation

The creation of a routing overlay network on the Internet requires the identification of shorter detour paths between end hosts in comparison to the default path available. These detour paths are typically the edges forming a Triangle Inequality Violation (TIV), an artifact of the Internet delay space where the sum of latencies across an intermediate hop is lesser than the direct latency between the pair of end hosts. These violations are caused mainly due to interdomain routing policies between Autonomous Systems (ASes) and AS peering through Internet eXchange Points (IXPs). Identifying detours for a global overlay network requires large amounts of computational capabilities due to the sheer number of possible paths linking source and destination ASes. In this work, we use parallel programming paradigms to exploit the massively parallel capabilities of analyzing the large network measurement datasets made available to the network research community by CAIDA. We study Internet routes traversing IXPs and measure potential TIVs created by these paths. Large scale analysis of the dataset is carried out by implementing an efficient parallel solution on the CPU and then the general purpose graphics processor unit (GPGPU) as well. Both multicore CPU and GPGPU implementations can be carried out with ease on desktop environments with readily available software. We find both parallel solutions yield high improvements in speedup (2-35x) in comparison to the serial methodologies thereby opening up the possibility of harnessing the power of parallel programming with readily available hardware. The large amount of data analyzed and studied helps draw various inferences for the networking research community in building future scalable Internet routing overlays with greater routing efficiencies.     

Analysis of point-to-point packet delay in an operational network

In this paper, we perform a detailed analysis of point-to-point packet delay in an operational tier-1 network. The point-to-point delay is the time experienced by a packet from an ingress to an egress point in an ISP, and it provides the most basic information regarding the delay performance of the ISP’s network. Using packet traces captured in the operational network, we obtain precise point-to-point packet delay measurements and analyze the various factors affecting them. Through a simple, step-by-step, systematic methodology and careful data analysis, we identify the major network factors that contribute to point-to-point packet delay and characterize their effect on the network delay performance. Our findings are: (1) delay distributions vary greatly in shape, depending on the path and link utilization; (2) after constant factors dependent only on the path and packet size are removed, the 99th percentile variable delay remains under 1 ms over several hops and under link utilization below 90% on a bottleneck; (3) a very small number of packets experience very large delay in short bursts.     

SoMR: A scalable distributed QoS multicast routing protocol

Many Internet multicast applications such as teleconferencing and remote diagnosis have Quality-of-Service (QoS) requirements. The requirements can be additive (end-to-end delay), multiplicative (loss rate), or of a bottleneck nature (bandwidth). Given such diverse requirements, it is a challenging task to build QoS-constrained multicast trees in a large network where no global network state is available. This paper proposes a scalable QoS multicast routing protocol (SoMR) that supports all three QoS requirement types. SoMR is scalable due to small communication overhead. It achieves favorable tradeoff between routing performance and routing overhead by carefully selecting the network sub-graph in which it searches for a path that can support the QoS requirements. The scope of search is automatically tuned based on the current network conditions. An early-warning mechanism helps detect and route around the long-delay paths in the network. The operations of SoMR are completely decentralized. They rely only on the local state stored at each router.     

CCIPCA-OPCSC: An online method for detecting shared congestion paths

It is very useful to detect network paths sharing the same bottleneck for improving efficiency and fairness of network resource usage. Existing techniques have been designed to detect shared congestion between a pair of paths with a common source or destination point. And they are poor in scalability and not applicable to online detection. To cope with these problems, a novel method called CCIPCA-based Online Path Clustering by Shared Congestion (CCIPCA-OPCSC) is proposed to detect shared congestion paths, whose essence lies in the use of a novel eigenvector projection analysis (EPA). First, the delay measurement data of each path are mapped into a point in a new, low-dimensional space based on the correlation among paths reflected by the eigenvectors and eigenvalues in the process of PCA. In this new space, points are close to each other if the corresponding paths share congestion. CCIPCA is also introduced to compute the eigenvectors and eigenvalues incrementally. Second, the clustering analysis is applied on these points so as to identify shared congestion paths accurately. CCIPCA-OPCSC has low computational complexity and can fulfill the requirement of online detection. This method is evaluated by NS2 simulations and experiments on the PlanetLab testbed over the Internet. The results demonstrate that this novel method is feasible and effective.     

Misleading stars: what cannot be measured in the internet?

Traceroute measurements are one of the main instruments to shed light onto the structure and properties of today’s complex networks such as the Internet. This article studies the feasibility and infeasibility of inferring the network topology given traceroute data from a worst-case perspective, i.e., without any probabilistic assumptions on, e.g., the nodes’ degree distribution. We attend to a scenario where some of the routers are anonymous, and propose two fundamental axioms that model two basic assumptions on the traceroute data: (1) each trace corresponds to a real path in the network, and (2) the routing paths are at most a factor $1/\alpha $ off the shortest paths, for some parameter $\alpha \in (0,1]$ . In contrast to existing literature that focuses on the cardinality of the set of (often only minimal) inferrable topologies, we argue that a large number of possible topologies alone is often unproblematic, as long as the networks have a similar structure. We hence seek to characterize the set of topologies inferred with our axioms. We introduce the notion of star graphs whose colorings capture the differences among inferred topologies; it also allows us to construct inferred topologies explicitly. We find that in general, inferrable topologies can differ significantly in many important aspects, such as the nodes’ distances or the number of triangles. These negative results are complemented by a discussion of a scenario where the trace set is best possible, i.e., “complete”. It turns out that while some properties such as the node degrees are still hard to measure, a complete trace set can help to determine global properties such as the connectivity.     

Demo abstract: towards extracting semantics by visualizing large traceroute datasets

Many Internet mapping projects have used traceroute as a measurement primitive and have generated hundreds of millions of traceroute samples used, among many purposes, for network debugging, troubleshooting and Internet mapping. In this work, we advocate the use of visualization as a means to extract semantics from large sets of traceroute data collected from large-scale traceroute campaigns. Using subsets of these datasets consisting of all traceroutes traversing a particular Internet eXchange Point (IXP), we illustrate how visualization allows us to (1) examine existing large datasets in a different way and (2) present different views of the data that can provide new insights and lead to new questions.     

Locating congested segments over the Internet by clustering the delay performance of multiple paths

Delay variation-based detection and location of congestion in a large network is considered. Since the Internet is still highly prone to performance deterioration due to transient large delays, locating a part of the network (segments) responsible is vital to ensure that Internet Service Providers can mitigate or prevent such performance deterioration. In the proposed method, the end-to-end packet delays from multiple origins to multiple destinations are actively and continuously measured. By analyzing those data on delay variation along each monitored path, congestion is detected by finding a delay performance deterioration worse than a predefined criteria and a congested segment responsible could be inferred by finding a set of paths among which delay variations are strongly correlated. This is a network tomographic approach based on a clustering technique that effectively tackles the correlation among packet delay variation along individual paths. The proposed method was evaluated through a real-world long-term experiment on the Japan’s commercial Internet, and was shown to have considerable potential to promptly locate congested segments through various analyses on the experimental results.     

FlowTrace: measuring round-trip time and tracing path in software-defined networking with low communication overhead

In today’s networks, load balancing and priority queues in switches are used to support various quality-of-service (QoS) features and provide preferential treatment to certain types of traffic. Traditionally, network operators use ‘traceroute’ and ‘ping’ to troubleshoot load balancing and QoS problems. However, these tools are not supported by the common OpenFlow-based switches in software-defined networking (SDN). In addition, traceroute and ping have potential problems. Because load balancing mechanisms balance flows to different paths, it is impossible for these tools to send a single type of probe packet to find the forwarding paths of flows and measure latencies. Therefore, tracing flows’ real forwarding paths is needed before measuring their latencies, and path tracing and latency measurement should be jointly considered. To this end, FlowTrace is proposed to find arbitrary flow paths and measure flow latencies in OpenFlow networks. FlowTrace collects all flow entries and calculates flow paths according to the collected flow entries. However, polling flow entries from switches will induce high overhead in the control plane of SDN. Therefore, a passive flow table collecting method with zero control plane overhead is proposed to address this problem. After finding flows’ real forwarding paths, FlowTrace uses a new measurement method to measure the latencies of different flows. Results of experiments conducted in Mininet indicate that FlowTrace can correctly find flow paths and accurately measure the latencies of flows in different priority classes.     

On the impact of routing matrix inconsistencies on statistical path monitoring in overlay networks

Overlay networks can be used to find working paths when direct underlay paths are anomalously slow, e.g. because of a network fault. Overlay paths should not use links that are involved in a fault, so choosing which overlay path to use often requires path monitoring, which introduces an overhead. By using a routing matrix ‘M’ to define which links are used in each path, and sorting the matrix according to the degree of independence of paths, we can choose a reduced set of paths to monitor, and so reduce overheads. The state of the unmonitored paths are then predicted using statistical estimation techniques based on information inferred from the monitored paths. However, such methods assume knowledge of routing matrix. We investigate the impact on such methods when knowledge of the routing matrix is only approximate, e.g. as obtained using simple tools like traceroute which have been previously blamed for incorrect mapping of the topology of real world IP networks. This paper investigates the impact of routing matrix errors on such statistical path estimation approaches. We show that mitigation or removal of such errors leads to improved path metric prediction and anomaly detection.     

A fuzzy-based approach to remove clock skew and reset from one-way delay measurement

One-way delay (OWD) traces are important measurements for analyzing end-to-end performance on the Internet. It is still a great challenge to provide a scalable solution for large-scale OWD measurement. Because the clocks at end systems are usually not synchronized, the OWD measurements are often inaccurate. For the more challenging case with clock resets to some reference times during the measurement, the OWD measurements are more inaccurate. Furthermore, the measurement data often exhibit considerable network-induced noise when the network is heavily loaded. All the existing OWD measurement techniques, such as linear programming and convex-hull approach (CHA), try to solve this problem by deterministic mathematics model. However, they often fail to distinguish clock resets from temporary Internet congestion. Based on the fuzzy-clustering analysis, this paper proposes a new algorithm to estimate and remove the clock skews and resets from measurement results. This algorithm has been implemented as a tool called fuzzy-based OWD corrector (FOC). The paper then presents OWD measurements of several Internet paths using FOC. Numerical experiments demonstrate that FOC is more accurate and robust than the existing techniques. FOCs computation complexity O(N) is similar to that of CHA and its computing time is much less than that of convex-hull technique.