嵌入式小区集中器网络接口的设计研究

小区集中器是智能小区电力管理信息化的重要环节,它实现了电力管理自上而下和自下而上的信息畅通。小区集中器采用ARMS3C2440为处理器核心,内嵌Linux操作系统为软件开发平台,通过互联网与管理部门上位机通讯。重点研究了小区集中器以太网接口部分,并且通过实验测试小区集中器的网络通讯功能,其结果验证了软硬件设计的正确性。     

Worst-case traffic in a tree network of ATM multiplexers

We study tree networks of discrete-time queues loaded with periodic traffic sources. By using the so-called Benes method, exact closed-form expressions are obtained for the queue length distributions. The models developed can be used to study the superposition of periodic sources emitting bursts of cells in ATM networks. The results obtained show the significant effect that this kind of traffic can have on the performance of these systems     

Cluster processes: a natural language for network traffic

We introduce a new approach to the modeling of network traffic, consisting of a semi-experimental methodology combining models with data and a class of point processes (cluster models) to represent the process of packet arrivals in a physically meaningful way. Wavelets are used to examine second-order statistics, and particular attention is paid to the modeling of long-range dependence and to the question of scale invariance at small scales. We analyze in depth the properties of several large traces of packet data and determine unambiguously the influence of network variables such as arrival patterns, durations, and volumes of transport control protocol (TCP) flows and internal flow structure. We show that session-level modeling is not relevant at the packet level. Our findings naturally suggest the use of cluster models. We define a class where TCP flows are directly modeled, and each model parameter has a direct meaning in network terms, allowing the model to be used to predict traffic properties as networks and traffic evolve. The class has the key advantage of being mathematically tractable, in particular, its spectrum is known and can be readily calculated, its wavelet spectrum deduced, interarrival distributions can be obtained, and it can be simulated in a straightforward way. The model reproduces the main second-order features, and results are compared against a simple black box point process alternative. Discrepancies with the model are discussed and explained, and enhancements are outlined. The elephant and mice view of traffic flows is revisited in the light of our findings.     

Self-similar traffic and network dynamics

One of the most significant findings of traffic measurement studies over the last decade has been the observed self-similarity in packet network traffic. Subsequent research has focused on the origins of this self-similarity, and the network engineering significance of this phenomenon. This paper reviews what is currently known about network traffic self-similarity and its significance. We then consider a matter of current research, namely, the manner in which network dynamics (specifically, the dynamics of transmission control protocol (TCP), the predominant transport protocol used in today's Internet) can affect the observed self-similarity. To this end, we first discuss some of the pitfalls associated with applying traditional performance evaluation techniques to highly-interacting, large-scale networks such as the Internet. We then present one promising approach based on chaotic maps to capture and model the dynamics of TCP-type feedback control in such networks. Not only can appropriately chosen chaotic map models capture a range of realistic source characteristics, but by coupling these to network state equations, one can study the effects of network dynamics on the observed scaling behavior We consider several aspects of TCP feedback, and illustrate by examples that while TCP-type feedback can modify the self-similar scaling behavior of network traffic, it neither generates it nor eliminates it     

Multiscale nature of network traffic

The complexity and richness of telecommunications traffic is such that one may despair to find any regularity or explanatory principles. Nonetheless, the discovery of scaling behavior in teletraffic has provided hope that parsimonious models can be found. The statistics of scaling behavior present many challenges, especially in nonstationary environments. In this article, we overview the state of the art in this area, focusing on the capabilities of the wavelet transform as a key tool for unraveling the mysteries of traffic statistics and dynamics     

Designing a green optical network unit using ARMA-based traffic prediction for quality of service-aware traffic

The optical access networks (OANs) provide an attractive solution to the bandwidth bottleneck problem of the last mile. However, it has been proved (Baliga et al. in J Lightwave Technol 27(13):2391–2403, 2009; Baliga et al. in IEEE Commun Mag 49(6):70–77, 2011) that the OAN consumes a significant ratio of the total energy consumed in an optical networking scenario. This has provided incentive for inspection of energy-efficient schemes for OANs. It has been demonstrated in the literature that energy consumption figures of an OAN can be improved by either designing efficient hardware or employing better media access control (MAC) protocols. In this paper, we design a MAC protocol for OANs to ensure energy-efficiency in the presence of quality of service (QoS)-aware traffic. The proposed scheme incorporates traffic prediction-based selection of different sleep (energy-efficient) modes of operation, of the optical network units—ONUs (OAN end units). It also implements switching between different sleep modes to maintain high QoS with significant energy-efficiency figures. The discussed scheme requires processing at the ONU only and can work independent of the entire OAN (ONU assisted). Thus, our proposal is an attractive solution for the already deployed networks or even in green field deployment.     

神经网络在无线网络流量预测中的应用

无线互联网的流量数据非常分离且极其不稳定,混沌理论在其身上体现得特别明显,因此对无线网络流量进行预测具有一定难度。该文使用BP神经网络建立预测模型,在常规神经网络系统进行训练之前,需要对系统内部各个层次之间的连接权值以及阈值范围实行初始化操作,但是此操作将会影响神经网络最终收敛速度,有可能造成最终结果为非最优解,使得流量预测结果不是很理想。因此这里使用布谷鸟搜索优化方式对神经网络系统内各层之间链接值与阈值进行初始化操作,提高系统预测精度。该文使用遗传优化神经网络算法和粒子群优化神经网络算法建立同样的预测模型,并与该文研究的预测模型进行对比。实例分析结果表明,初期预测结果精度较高,与实际值比较吻合,但测试数据越靠后,预测值越不稳定,这主要是累计误差造成的。但总的来说,该文使用的布谷鸟优化BP神经网络预测模型的预测性能要优于由遗传算法和粒子群算法优化的BP神经网络。     

Analysis and modeling of traffic on a hybrid fiber-coax network

This paper presents the study carried out on the data traffic collected on the network of a cable operator based on hybrid fiber-coax technology, and the subsequent simulation model developed to predict the bandwidth requirements of the network channels. The paper starts with the analysis of the traffic measurements, taken over two periods of time in one year, on all the network's channels. This analysis identifies the main characteristics of the traffic, as well as some relationships between network parameters and their persistence over time. The paper proceeds to present the development of a simulation model, which represents the cable network. This model is built from the results of the traffic analysis and network parameters. The major challenge of this model is to predict the traffic on each channel of the cable network, related to parameters of the network configuration, such as the number of assigned subscribers to the channel and the time of day. To reach this goal, the simulation model has been developed with a modular structure, which gives it flexibility to adapt to changes in the network. The process followed involves the establishment of a traffic model, a system model, and finally, the validation of the results.     

Semi-supervised internet network traffic classification using a Gaussian mixture model

With a dramatic increase in the number and variety of applications running over the internet, it is very important to be capable of dynamically identifying and classifying flows/traffic according to their network applications. Meanwhile, internet application classification is fundamental to numerous network activities. In this paper, we present a novel methodology for identifying different internet applications. The major contributions are: (1) we propose a Gaussian mixture model (GMM)-based semi-supervised classification system to identify different internet applications; (2) we achieve an optimum configuration for the GMM-based semi-supervised classification system. The effectiveness of these proposed approaches is demonstrated through experimental results.     

宽带网络流量分析和管理

近年来,随着宽带互联网在全球的迅速发展和各种相关应用的快速普及,宽带互联网已成为人们日常工作生活中不可或缺的信息承载工具.然而,伴随着用户的正常应用流量,宽带网络上形形色色的异常流量也随之而来,影响到网络的正常运行,威胁用户主机的安全和正常使用,通过日常网络管理的实际应用,对互联网流量分析进行了综合介绍,重点介绍如何借助主流流量分析技术-NETFLOW,解决异常流量分析与处理等问题.     

一种网络流量平稳化方法

由于网络流量的不稳定性,会影响流量模型的准确性。平稳化的目的是使得流量成为平稳或更为接近平稳,以提高流量模型的准确性。差分是一种常用的平稳化方法,它对具有线性趋势的时间序列有很好的平稳化效果。本文提出一种统计分析的方法对流量进行平稳化处理,可以在一定程度上消除流量不稳定性对流量建模的影响。     

业务优先级感知的网络流量调度机制

针对网络中业务数据流过大、分布不均匀所造成的网络拥塞,提出一种优先级感知的动态网络流量调度机制.利用令牌桶算法,根据业务优先级的不同为不同业务分配不同速率的令牌,以实现业务优先级的划分;综合考虑业务的优先级及用户节点剩余缓存空间,对不同的业务采取不同的处理方式;同时,以流量到达因素、服务因素及节点缓存为指标定义了一种网络流量调度机制性能指标——分组丢失率.数值结果表明,所提机制可以对网络中业务优先级进行合理的划分,从而有效利用网络资源,预防网络拥塞,提升网络性能,为用户提供更加稳定可靠的网络服务.     

基于网络流量的特征分析

网络流量在互联网发展不断深化的背景下表现出了极为重要的意义,也是对网络体系进行切入理解的重要渠道,还是设计、规划和管理网络的直接凭据。在多尺度下对网络流量的特征进行了针对性探究,以期对相关工作起到一定参考作用。     

WCDMA网络高话务量场景下的优化措施

随着WCDMA网络的不断完善,用户的不断增加,在人口密集的地方出现高话务量的场景下,从而导致不同程度的网络拥塞。特别是在一些大型集会场所高话务量场景,如何解决高话务增长,防止网络拥塞。现结合日常保障工作中遇到的高话务量问题,分析网络容量相关资源、网络负荷功能性等来进行解决网络在高话务量冲击下的优化,为今后更进一步的做好WCDMA网络通信保障工作打下基础。     

小波系数感知的网络流量预测机制

对网络流量的精确预测,可以准确把握网络运行趋势,及时防范网络故障。针对长期网络流量预测准确度低,收敛速度慢的问题,提出一种小波系数感知的网络流量预测(WCNTP)机制。借助重标极差(R/S)序列分析法初步评估网络流量在大时间尺度上的统计特性;利用离散小波变换将非平稳的网络流量分解为多个相对平稳的流量序列;利用分数自回归求和滑动(FARIMA)模型对网络流量进行预测。结果表明,所提机制在长期网络流量预测过程中,具有较高的准确度且收敛速度快,能够精确评估网络性能,在保证网络平稳运行的同时,提高网络服务质量。     

Autonomic traffic engineering for network robustness

The continuously increasing complexity of communication networks and the increasing diversity and unpredictability of traffic demand has led to a consensus view that the automation of the management process is inevitable. Currently, network and service management techniques are mostly manual, requiring human intervention, and leading to slow response times, high costs, and customer dissatisfaction. In this paper we present AutoNet, a self-organizing management system for core networks where robustness to environmental changes, namely traffic shifts, topology changes, and community of interest is viewed as critical. A framework to design robust control strategies for autonomic networks is proposed. The requirements of the network are translated to graph-theoretic metrics and the management system attempts to automatically evolve to a stable and robust control point by optimizing these metrics. The management approach is inspired by ideas from evolutionary science where a metric, network criticality, measures the survival value or robustness of a particular network configuration. In our system, network criticality is a measure of the robustness of the network to environmental changes. The control system is designed to direct the evolution of the system state in the direction of increasing robustness. As an application of our framework, we propose a traffic engineering method in which different paths are ranked based on their robustness measure, and the best path is selected to route the flow. The choice of the path is in the direction of preserving the robustness of the network to the unforeseen changes in topology and traffic demands. Furthermore, we develop a method for capacity assignment to optimize the robustness of the network.     

宽带网络流量分析和管理

近年来，随着宽带到联网在全球的迅速发展和各种相关应用的快速普及．宽带互联网已成为人们日常工作生活中不可或缺的信息承载工具。然而．伴随着用户的正常应用流量．宽带网络上形形色色的异常流量也随之而来．影响到网络的正常运行．威胁用户主机的安全和正常使用．通过日常网络管理的实际应用．对互联网流量分析进行了综合介绍．重点介绍如何借助主流流量分析技术-NETFLOW．解决异常流量分析与处理等问题。     

Specification as a Product for Export

The translation of a specification into a foreign language is often unsatisfactory. The factors contributing to this are discussed. Some are avoidable; others can only be attenuated. Some rules are suggested which, if applied when the document is first written, will reduce the risk of errors later.     

基于小波神经网络的网络流量预测研究

主要采用小波神经网络的相关理论和方法对某小区网络的访问流量情况进行建模和预测。利用收集到的网络流量变化情况作为小波网络的训练样本,成功实现了该网络的流量预测。试验仿真结果表明,构建的小波神经网络模型可以很好地实现对网络流量的高精度预测。