首页 | 官方网站   微博 | 高级检索  
相似文献
 共查询到18条相似文献,搜索用时 93 毫秒
1.
自相似网络业务流量的研究与实现   总被引:7,自引:0,他引:7  
为了准确测试和评估网络交换设备及其调度算法的性能,一个能够真实反映实际网络业务流量特点的业务流量产生系统是十分必要的。近年来通过对大量网络业务流量的测量和分析,人们认识到网络业务流量呈现为长相关、自相似的特性,而非泊松过程。将这一特性和现有的业务流量描述模型相结合,利用具有重尾特性的概率分布函数:Pareto分布和截尾重尾分布,构造了在宏观上表现为自相似特性的业务流量模型。针对路由交换机构调度算法的性能测试的实际需要,建立了一个可用于软件测试的网络业务流量产生系统。  相似文献   

2.
针对传统数据中心电互连网络在应对业务动态流量时存在适应性差的问题,文章提出并验证了一种可以根据网络流量波动进行网络拓扑自优化重构的机制。文章所提机制通过网络仿真系统与深度强化学习模型的迭代交互,实现了对拓扑结构与业务流量分布关系的持续训练,进而在实际系统中,深度强化学习模型,根据软件定义网络控制器实时收集的业务流量分布信息,实现了网络拓扑的自动优化重构,进而提升了网络性能。实验结果表明,针对给定的流量强度,采用深度强化学习进行训练后的模型可以一步输出优化的网络拓扑结构,降低了平均网络延迟和丢包率。  相似文献   

3.
重尾ON/OFF源模型生成自相似业务流研究   总被引:23,自引:0,他引:23  
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。  相似文献   

4.
以现有固网的网络流量和网络业务模型为基础,对下一代移动互联网络Mesh的基站流量模型和网络带宽需求进行研究.分析了典型区域内网民的http业务、gaming业务、video业务的流量模型和网民上网的行为概率分布,估算出一天内的基站总流量带宽分布,为未来mesh网络的规划和组建提供数据依据.  相似文献   

5.
随着互联网技术的不断发展以及网络规模的不断扩大,新的网络业务层出不穷,为了保障用户服务质量,准确快速地对业务流量进行分类是目前的研究重点。传统业务识别方法多以协议或具体业务为分类依据,应用性较低。文章结合业务流量特征和机器学习方法,提出了一种基于生成对抗网络(GAN)和极端梯度增强(XGBoost)融合的业务流量识别方法。该方法首先提取代表业务资源需求的流量特征;然后通过改进GAN算法扩充少数类样本,解决业务识别过程中出现的数据集分布不平衡导致的模型准确率低的问题;最后通过随机森林算法进行特征选择,并利用XGBoost算法完成模型训练。结果表明,该方法对业务识别的准确率达到了97.32%。  相似文献   

6.
本文提出了一种降低ATM网络拥塞率的业务流量分布自适应优化控制方法,建立了一种碰撞函数型的新的距离测度,并建立了多种业务环境下拥塞概率的分析模型,得出了多端口拥塞的计算公式,研究结果表明,业务流量分布的自适应优化控制技术能很好的改善ATM网络内的业务流量分布,大大降低了信道拥塞率。  相似文献   

7.
基于OPNET的IPTV业务网络仿真与实现   总被引:2,自引:2,他引:0  
IPTV业务体现了三网融合的要求。文中通过对IPTV业务及承载网络架构的研究,利用OPNET仿真软件对网络中jPTV的典型业务进行仿真,得出了网络模型中各主干链路流量的对比分析并讨论了流量差异化的原因,同时,给出了IPTV视频类业务和其它数据业务的流量分析。仿真结果表明,网络的设计方案是可行的,实现了IPTV的基本业务。  相似文献   

8.
传统模式下的网络仿真,报文到达均服从的是一种具有短相关特性的泊松分布。而经过大量业务流量监测表明,网络流量实际呈现出的确是一种具有长相关特性的自相似分布,这种特性对网络流量建模、性能分析、接纳控制等产生了重要影响。在对自相似特性深入分析的基础上,利用分型布朗运动模型的RMD算法产生自相似序列来模拟网络业务,并对该业务流特性下的交换式以太网进行了仿真实验。结果表明业务量的自相似性对交换式网络的各项性能影响很大,这与传统流量模型形成鲜明对比。  相似文献   

9.
信息通信融和趋势下电力通信网络存在诸多问题,面临转型压力。电力通信网体系架构是实现智能电网基础,建立全业务模型是网络规划设计前提。文章对电力通信业务进行分类和特征分析,对各级变电站、营业厅、局大楼等通信节点进行业务断面流量分析,并以浙江电力为例,给出了全业务流量模型。  相似文献   

10.
根据政企专线业务对安全要求级别、业务颗粒大小的不同,提出满足需求的相应承载网络。重点分析政企专线业务通过IPRAN(IP化的移动回传网络)承载的策略,针对本地、省内(跨地市)、点到多点等不同场景的专线业务,提出相应的业务开放、链路保护机制等业务模型,以及IPRAN网络与传统专线承载网络互通方案。结合POP(综合业务接入点)点建设原则和接入层流量测算模型,根据政企客户目标群体的分布不同,提出相应的IPRAN政企业务建设模型,为IPRAN承载政企专线提供参考。  相似文献   

11.
自相似网络通信量模型研究综述   总被引:7,自引:0,他引:7  
越来越多的研究表明网络通信量不是Markov过程,而是在任意时间尺度上都具有突发特性,即自相似特性。描述网络通信量的数学模型主要有自相似和长相关结构。网络的某些参数服从重尾分布,从而导致网络通信量时间尺度上的突发特性。该文分析了传统网络通信量模型和性能分析的弊端,描述了新型网络通信量模型应该具有的基本特征。本文重点研究了网络自相似通信量相关的ON/OFF模型、用户访问概率模型和网络流量闭环模型,讨论了相关的研究方向,并总结了在研究网络通信量模型的过程中应该注意的原则和问题。  相似文献   

12.
Most studies of Internet traffic rely on observations from a single link. The corresponding traffic dynamics has been studied for more than a decade and is well understood. The study of how traffic on the Internet topology, on the other hand, is poorly understood and has been largely limited to the distribution of traffic among source-destination pairs inside the studied network, also called the traffic matrix. In this paper, we make a first step towards understanding the way traffic gets distributed onto the whole topology of the Internet. For this, we rely on the traffic seen by a transit network, for a period of more than a week. As we are still at the stage of understanding the topological traffic distribution, we do not try to model the traffic dynamics. Rather we concentrate on understanding the complexity of describing the traffic observed by a transit network, how it maps onto the AS-level topology of the Internet and how it changes over time. For this, we rely on well-known tools of multi-variate analysis and multi-resolution analysis. Our first observation is that the structure of the Internet topology highly impacts the traffic distribution. Second, our attempts at compressing the traffic on the topology through dimension reduction suggests two options for traffic modeling: (1) to ignore links on the topology for which we do not see much traffic, or (2) to ignore time scales smaller than a few hours. In either case, important properties of the traffic might be lost, so might not be an option to build realistic models of Internet traffic. Realistic models of Internet traffic on the topology are not out of reach though. In this paper, we identify two properties such models should have: (1) use a compact representation of the dependencies of the traffic on the topology, and (2) be able to capture the complex multi-scale nature of traffic dynamics on different types of links.  相似文献   

13.
Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. This paper proposes a novel Bayesian-based algorithm within the framework of wavelet analysis, which reduces speckle in SAR images while preserving the structural features and textural information of the scene. First, we show that the subband decompositions of logarithmically transformed SAR images are accurately modeled by alpha-stable distributions, a family of heavy-tailed densities. Consequently, we exploit this a priori information by designing a maximum a posteriori (MAP) estimator. We use the alpha-stable model to develop a blind speckle-suppression processor that performs a nonlinear operation on the data and we relate this nonlinearity to the degree of non-Gaussianity of the data. Finally, we compare our proposed method to current state-of-the-art soft thresholding techniques applied on real SAR imagery and we quantify the achieved performance improvement.  相似文献   

14.
Modeling SAR images with a generalization of the Rayleigh distribution   总被引:11,自引:0,他引:11  
Synthetic aperture radar (SAR) imagery has found important applications due to its clear advantages over optical satellite imagery one of them being able to operate in various weather conditions. However, due to the physics of the radar imaging process, SAR images contain unwanted artifacts in the form of a granular look which is called speckle. The assumptions of the classical SAR image generation model lead to a Rayleigh distribution model for the histogram of the SAR image. However, some experimental data such as images of urban areas show impulsive characteristics that correspond to underlying heavy-tailed distributions, which are clearly non-Rayleigh. Some alternative distributions have been suggested such as the Weibull, log-normal, and the k-distribution which had success in varying degrees depending on the application. Recently, an alternative model namely the alpha-stable distribution has been suggested for modeling radar clutter. In this paper, we show that the amplitude distribution of the complex wave, the real and the imaginery components of which are assumed to be distributed by the alpha-stable distribution, is a generalization of the Rayleigh distribution. We demonstrate that the amplitude distribution is a mixture of Rayleighs as is the k-distribution in accordance with earlier work on modeling SAR images which showed that almost all successful SAR image models could be expressed as mixtures of Rayleighs. We also present parameter estimation techniques based on negative order moments for the new model. Finally, we test the performance of the model on urban images and compare with other models such as Rayleigh, Weibull, and the k-distribution.  相似文献   

15.
The user clients for accessing Internet are increasingly shifting from desktop computers to cellular devices. To be competitive in the rapidly changing market, operators, Internet service providers and application developers are required to have the capability of recognizing the models of cellular devices and understanding the traffic dynamics of cellular data network. In this paper, we propose a novel Jaccard measurement‐based method to recognize cellular device models from network traffic data. This method is implemented as a scalable paralleled MapReduce program and achieves a high accuracy, 91.5%, in the evaluation with 2.9 billion traffic records collected from the real network. Based on the recognition results, we conduct a comprehensive study of three characteristics of network traffic from device model perspective, the network access time, the traffic volume, and the diurnal patterns. The analysis results show that the distribution of network access time can be modeled by a two‐component Gaussian mixture model, and the distribution of traffic volumes is highly skewed and follows the power law. In addition, seven distinct diurnal patterns of cellular device usage are identified by applying unsupervised clustering algorithm on the collected massive traffic data. Copyright © 2014 John Wiley & Sons, Ltd.  相似文献   

16.
网络流量的联合多重分形模型及特性分析   总被引:1,自引:1,他引:0       下载免费PDF全文
魏进武  邬江兴  陈庶樵 《电子学报》2004,32(9):1459-1463
网络尺度行为的发现提供了用数学模型方法研究网络流量特性的可能性.本文基于连乘瀑布过程与K分布过程提出了联合多重分形(JMF)网络流量模型,该模型以尺度函数与矩因子的联合作为主要特征函数来研究网络流量的特性.理论分析及由实测网络流量数据的仿真结果表明,JMF模型可以较客观地同时描述网络流量短期分形行为与长期自相似行为,且实现复杂度小.其中尺度函数能够刻画时间尺度对流量特性的影响,矩因子描述了同一时间尺度上流量突发性的变化,二者的联合较好地描述了网络流量的短期行为,而模型的统计特性则刻画了流量的长期行为特征.  相似文献   

17.
Kim  Meejoung 《Wireless Networks》2020,26(8):6189-6202

In this paper, we introduce the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) as a network traffic prediction model. As the INGARCH is known as a non-linear analytical model that could capture the characteristics of network traffic such as Poisson packet arrival and long-range dependence property, INGARCH seems to be an adequate model for network traffic prediction. Based on the investigation for the traffic arrival process in various network topologies including IoT and VANET, we could confirm that assuming the Poisson process as packet arrival works for some networks and environments of networks. The prediction model is generated by estimating parameters of the INGARCH process and predicting the Poisson parameters of future-steps ahead process using the conditional maximum likelihood estimation method and prediction procedure, respectively. Its performance is compared with those of three different models; autoregressive integrated moving average, GARCH, and long short-term memory recurrent neural network. Anonymized passive traffic traces provided by the Center for Applied Internet Data Analysis are used in the experiment. Numerical results show that the proposed model predicts better than the three models in terms of measurements used in prediction models. Based on the study, we can conclude the followings: INGARCH can capture the characteristics of network traffic better than other statistic models, it is more tractable than neural networks (NNs) overcoming the black-box nature of NNs, and the performances of some statistical models are comparable or even superior to those of NNs, especially when the data is insufficient to apply deep NNs.

  相似文献   

18.
We investigate in this paper the time evolution and the composition in terms of applications of traffic in two different networks, namely the Renater network, dedicated to the French academic and research community, and the France Télécom backbone network supporting commercial traffic. For each network, we present the time evolution of traffic in terms of applications, the associated pie charts for global results, as well as, for each detected application, its flow size distribution, that should have an impact on the traffic nature (self-similarity or long range dependence due to the heavy tail of flow size distribution). Based on these results, this paper presents a discussion on the differences between academic and commercial traffic in terms of usage, as well as possible solutions against lrd and its associated degradation of network performance. For traffic analysis, we propose a new method of classifying traffic according to applications, which relies on applicative protocols recognition instead on the iana ports numbers.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号