无线Mesh网络流量自相似性研究

近年来,无线Mesh网络已成为一个倍受关注的研究领域.对无线Mesh网络流量特性的研究将有助于网络协议的研究、评估,以及网络结构的规划和建设.本文通过对无线Mesh测试网上采集的数据包进行统计分析,揭示了网络流量具有自相似的特性.同时,解释了无线Mesh网络流量自相似性是由网络中具有重尾分布特性的多个流叠加形成的,并通过仿真进一步分析了节点移动性对流量自相似性的影响.     

MANET中路由算法对通信流自相似特性的影响

着重分析无线多跳移动网络中,不同路由算法对多媒体通信流自相似特性的影响.通过NS2仿真产生基于不同路由算法的通信流数据,运用V-T法和R/S图法分别分析其突发性参数.结果表明,基于表驱动的DSDV自相似参数最大,而分簇路由算法CBRP自相似参数最小,更有利于实时流媒体的传输.研究对提高无线网络路由协议的性能和多媒体服务的质量具有重要意义.     

Study of the Effect of the Wireless Gateway on Incoming Self-Similar Traffic

It has been well established by now that high-speed wireline traffic exhibits self-similar behavior. Due to the important consequences of traffic self-similarity in network design, several studies have assumed that wireless traffic is also self-similar and looked at its effects on network performance. However, due to factors such as power limitations and the wireless channel, it is not straightforward that wireline traffic will remain self-similar as it enters the wireless network. This paper provides an analytical study of the propagation of traffic characteristics as wireline traffic is passed to the wireless network through a gateway. The analysis takes into account buffering and repacking operations performed at the gateway, and models for wireline traffic and the wireless channel. We consider two server models, an instant transfer model, and an energy-conserving one. We show that in most cases, in response to self-similar wireline traffic the gateway will produce self-similar wireless traffic. However, when the gateway operates under an energy-conserving mode and if it has a large buffer, wireline traffic such as non-real-time variable-bit-rate traffic will result in non-self-similar wireless traffic. We also study the delays of packets passing through a gateway that is fed by self-similar traffic and show that their survival function has an asymptotically power-law tail with index smaller than 2.     

Ad hoc wireless network traffic-self-similarity and forecasting

Lots of works have been carried out to discuss the self-similarity of Ethernet and World Wide Web traffic. In this letter, we study the ad hoc wireless network traffic collected in an ad hoc network (AHN) testbed and show that the ad hoc wireless network traffic is self-similar, which validates that AHN traffic is forecastable because self-similar time-series can be forecasted. We apply a fuzzy logic system to ad hoc wireless network traffic forecasting and simulation results show that it performs much better than does an LMS adaptive filter. All these studies are very important for evaluating network capacity and determining the battery power mode based on the forecasted traffic workload     

重尾ON/OFF源模型生成自相似业务流研究

因为传统模型没有考虑网络业务流量各种时间尺度都具有突发性,因此是不完善的,一系列的测量结果表明,网络业务流量显示自相似性,重尾分布ON／OFF模型能比较好地解释自相似业务流的产生原因,可以把LAN上的业务分解为多个活动（active)主机对之间的业务流,本文通过系统的仿真实验研究了重尾ON／OFF源模型生成自相似流的机理,并与理论结果进行了对比,而且补充了理论结果。     

TCP批量业务的自相似特性

通过对简单网络拓扑结构下不同参数TCP批量传输实测流量数据的统计分析,研究了TCP批量稳定流量的自相似特性.分析结果表明,在10BaseT环境下,TCP批量传输的流量比较稳定,没有明显的突发性与自相似特性.而在100BaseTx环境下,TCP批量传输的流量表现出明显的突发性与自相似性,其自相似性强弱程度与通信方式、网卡缓存大小密切相关.同样条件,全双工TCP流自相似强度大于半双工流;而网卡缓存越少,TCP流突发性与自相似性越强.     

Self-similarity through high-variability: statistical analysis ofEthernet LAN traffic at the source level

A number of empirical studies of traffic measurements from a variety of working packet networks have demonstrated that actual network traffic is self-similar or long-range dependent in nature-in sharp contrast to commonly made traffic modeling assumptions. We provide a plausible physical explanation for the occurrence of self-similarity in local-area network (LAN) traffic. Our explanation is based on convergence results for processes that exhibit high variability and is supported by detailed statistical analyzes of real-time traffic measurements from Ethernet LANs at the level of individual sources. This paper is an extended version of Willinger et al. (1995). We develop here the mathematical results concerning the superposition of strictly alternating ON/OFF sources. Our key mathematical result states that the superposition of many ON/OFF sources (also known as packet-trains) with strictly alternating ON- and OFF-periods and whose ON-periods or OFF-periods exhibit the Noah effect produces aggregate network traffic that exhibits the Joseph effect. There is, moreover, a simple relation between the parameters describing the intensities of the Noah effect (high variability) and the Joseph effect (self-similarity). An extensive statistical analysis of high time-resolution Ethernet LAN traffic traces confirms that the data at the level of individual sources or source-destination pairs are consistent with the Noah effect. We also discuss implications of this simple physical explanation for the presence of self-similar traffic patterns in modern high-speed network traffic     

Optimal Routing for Wireless Mesh Networks With Dynamic Traffic Demand

Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broadband Internet access. Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best routing solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for wireless mesh network routing under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By studying the traces collected at wireless access points, we first present a traffic estimation method which predicts future traffic demand based on its historical data using time-series analysis. This method provides not only the mean value of the future traffic demand estimation but also its statistical distribution. We further investigate the optimal routing strategies for wireless mesh network which take these two forms of traffic demand estimations as inputs. The goal is to balance the traffic load so that minimum congestion will be incurred. This routing objective could be transformed into the throughput optimization problem where the throughput of aggregated flows is maximized subject to fairness constraints that are weighted by the traffic demands. Based on linear programming, we present two routing algorithms which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate the traffic dynamics in mesh network routing.     

Packet-level traffic measurements from the Sprint IP backbone

Network traffic measurements provide essential data for networking research and network management. In this article we describe a passive monitoring system designed to capture GPS synchronized packet-level traffic measurements on OC-3, OC-12, and OC-48 links. Our system is deployed in four POP in the Sprint IP backbone. Measurement data is stored on a 10 Tbyte storage area network and analyzed on a computing cluster. We present a set of results to both demonstrate the strength of the system and identify recent changes in Internet traffic characteristics. The results include traffic workload, analyses of TCP flow round-trip times, out-of-sequence packet rates, and packet delay. We also show that some links no longer carry Web traffic as their dominant component to the benefit of file sharing and media streaming. On most links we monitored, TCP flows exhibit low out-of-sequence packet rates, and backbone delays are dominated by the speed of light.     

A nonstationary traffic train model for fine scale inference from coarse scale counts

The self-similarity of network traffic has been convincingly established based on detailed packet traces. This fundamental result promises the possibility of solving on-line and off-line traffic engineering problems using easily collectible coarse time-scale data, such as simple network management protocol measurements. This paper proposes a statistical model that supports predicting fine time-scale behavior of network traffic from coarse time-scale aggregate measurements. The model generalizes the commonly used fractional Gaussian noise process in two important ways: (1) it accommodates the recurring daily load patterns commonly observed on backbone links and (2) features of long range dependence and self-similarity are modeled only at fine time scales and are progressively damped as the time period increases. Using the data we collected on the Chinese Education and Research Network, we demonstrate that the proposed model fits 5-min data and generates 10-s aggregates that are similar to actual 10-s data.