Practical Time-Scale Fitting of Self-Similar Traffic with Markov-Modulated Poisson Process

Recent measurements of packet/cell streams in multimedia communication networks have revealed that they have the self-similar property and are of different characteristics from traditional traffic streams. In this paper, we first give some definitions of self-similarity. Then, we propose a fitting method for the self-similar traffic in terms of Markov-modulated Poisson process (MMPP). We construct an MMPP as the superposition of two-state MMPPs and fit it so as to match the variance function over several time-scales. Numerical examples show that the variance function of the self-similar process can be well represented by that of resulting MMPPs. We also examine the queueing behavior of the resulting MMPP/D/1 queueing systems. We compare the analytical results of MMPP/D/1 with the simulation ones of the queueing system with self-similar input.     

Modeling and analysis of self-similar traffic source based on fractal-binomial-noise-driven Poisson process

~~Modeling and analysis of self-similar traffic source based on fractal-binomial-noise-driven Poisson process1. Will E L, Murad S T, Walter W, et al. On the self-similarity nature of Ethernet traffic (extended version). IEEE/ACM Transactions on Networking…     

Modeling MPEG Coded Video Traffic by Markov-Modulated Self-Similar Processes

Markov modulated self-similar processes are proposed to model MPEG video sequences that can capture the LRD (Long Range Dependency) characteristics of video ACF (Auto-Correlation Function). The basic idea is to decompose an MPEG compressed video sequence into three parts according to different motion/content complexity such that each part can individually be described by a self-similar process. Beta distribution is used to characterize the marginal cumulative distribution (CDF) of the self-similar processes. To model the whole data set, Markov chain is used to govern the transitions among these three self-similar processes. In addition to the analytical derivation, initial simulations have demonstrated that our new model can capture the LRD of ACF and the marginal CDF very well. Network cell loss rate using our proposed synthesized traffic is found to be comparable with that using empirical data as the source traffic.     

自相似业务模型下的队列分析--大偏差技术

近年来对许多实际运行中的网络（局域网和广域网）的测量与分析证实：真实的网络业务具有统计上的自相似性而且是长相关的。传统的排队理论均假定业务是Poisson或Markovian,因此是短相关的,那里的队列分析方法对自相似业务模型是不适用的。大偏差技术是对自相似业务模型进行队列分析的有效方法。本文应用大偏差技术对以FARIMA为业务模型的队列系统进行了分析,结果证明：队列长度尾分布渐进为Weibullian分布,而且与模型的短相关结构无关。     

基于自相似业务的网络TCP拥塞控制算法

本文通过对自相似网络模型的分析,采用TCP协议可以直接观测的回环时间（Bound Trip Time)来进行拥塞程度预测,提出一种改进的TCP拥塞控制算法。仿真证明,改进算法在一定程度上提高了网络的整体性能,在自相似程度较高时尤为明显。     

Interleaved Polling with Adaptive Cycle Time (IPACT): A Dynamic Bandwidth Distribution Scheme in an Optical Access Network

While in recent years backbone bandwidth has experienced substantial growth, little has changed in the access network. Last mile still remains the bottleneck between a high capacity LAN or home network and the backbone. Passive optical network (PON) is a technology viewed by many as an attractive solution to this problem.In this study, we discuss and evaluate design issues for PON access networks. Specifically, to drive the cost of an access network down, it is very important to have an efficient, scalable solution. We believe that a PON based on polling, with data encapsulated in Ethernet frames, possesses the best qualities, such as dynamic bandwidth distribution, use of a single downstream and a single upstream wavelength, ability to provision a fractional wavelength capacity to each user, and ease of adding a new user.To support dynamic bandwidth distribution, we propose an interleaved polling algorithm. We then suggest a scheme for in-band signaling that allows using a single wavelength for both downstream data and control message transmission.To obtain realistic simulation results, we generated synthetic traffic that exhibits the properties of self-similarity and long-range dependence. We then analyzed the network performance and its effect on various types of traffic, e.g., best-effort data traffic, VBR video traffic and CBR streams.     

基于马尔可夫决策过程的维修决策模型研究

提出了一种基于设备状况进行维修的模型.该模型包括随机失效和针对失效进行有计划检查、维修的过程.运用马尔可夫决策模型,提出了以费用-效果为指标的维修策略.     

基于FBM模型的自相似网络排队性能分析

利用能够反映自相似特性的FBM模型,采用G/D/1排队模型研究了自相似性对网络性能的影响,讨论了在Norros给出的缓冲区溢出概率公式的基础下,FBM模型为输入时,网络平均排队延迟的解析公式.对理论分形流量和实际测量流量进行了仿真实验,验证了结果的正确性和有效性.     

基于FrFT的网络流量Hurst参数估计器

实际的网络流量模型采用自相似模型,Hurst参数是序列长相关程度的度量。为方便工程上对网络流量的长相关性进行估计,介绍了网络流量自相似模型和Hurst参数小波分析法,建立了分数阶傅里叶变换（FrFT）与小波分析之间的联系,在此基础上介绍了一种新的基于FrFT的网络流量Hurst参数估计方法,并运用此方法设计了网络流量Hurst参数估计器。通过对白噪声和已知Hurst参数的实际网络流量数据进行估计,本方法可以有效估计随机时间序列的Hurst参数。     

自相似VBR视频源业务流的建模和仿真

该文在对实际VBR MPEG视频源统计特性分析的基础上,参照分形高斯噪声自相似(Fractional Gaussian Noise Self-Similar)模型产生方法,实现了对ATM网络中最主要业务流VBR视频源流的建模,提出了改进方法,使得对实际源的仿真不仅考虑到了长期相关性,同时也兼顾到了短期相关性。仿真结果表明,经改进的自相似VBR视频源模型是一种较理想的模型。     

基于离散小波变换的网络流量多重分形模型

网络流量过程中所蕴含的分形尺度特性对网络性能有显著的影响。因此研究能全面准确地刻画网络流量过程在小时间／空间尺度上的复杂奇异性特征和大时间／空间尺度上的长程依赖性特征的流量模型对Internet网络工程有重要的意义。本文对实测的流量数据(从著名的校园网和国内著名的ISP)进行了分析，利用小波技术构建了一个新的网络流量的多重分形模型，通过模拟验证，发现该新模型能以较简洁的形式捕捉实际网络流量特性，并具有刻画真实流量数据中的多重分形特征的能力。     

MPEG标准VBR视频业务新模型及其性能研究

迄今为止,MPEG-1标准下的可变比特率(VBR)视频业务建模问题还没有得到很好的解决.鉴于此,本文在分析这类业务统计特性的基础上,对这类业务提出了一种新的帧层模型.该模型可以很好地体现这类业务的一、二阶统计特性.通过使用该模型模拟实际视频信号,也显示了该模型在体现实际业务排队性能上的有效性.     

Scheduling in Optical WDM Networks Using Hidden Markov Chain Based Traffic Prediction

This paper presents the design and performance analysis of a predictor-based scheduling algorithm for optical wavelength division multiplexed (WDM) networks. WDM technology provides multiple, simultaneous and independent gigabit-per-second channels on a single fiber. A reservation-based multiple access control (MAC) protocol is considered here for a local area WDM network based on the passive star topology. The MAC protocol schedules reservation requests from the network nodes on the multiple channels. In previous work, we have presented an on-line scheduling algorithm for such a network. We have shown earlier that schedule computation time can significantly affect performance and the scheduling algorithms should be simple for better performance. In this work, we further improve system performance by using a hidden Markov chain based prediction algorithm. The objective here is to reduce the amount of time spent in computing the schedule by predicting traffic requests. Performance analysis based on discrete-event simulation, varying parameters such as number of nodes and channels is presented. The results show that the error of prediction is reasonable for most cases: more than 70% of the time, the error between actual request and predicted request is less than 20%. Network throughput is higher with the proposed prediction algorithm due to pipelining of schedule computation.     

Novel Approach for Modeling Wireless Fading Channels Using a Finite State Markov Chain

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal‐to‐noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first‐order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.