Results 1  10
of
36
WideArea Traffic: The Failure of Poisson Modeling
 IEEE/ACM TRANSACTIONS ON NETWORKING
, 1995
"... Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 widearea traces, investigating a number of widearea TCP arrival processes (session and con ..."
Abstract

Cited by 1772 (24 self)
 Add to MetaCart
(Show Context)
Network arrivals are often modeled as Poisson processes for analytic simplicity, even though a number of traffic studies have shown that packet interarrivals are not exponentially distributed. We evaluate 24 widearea traces, investigating a number of widearea TCP arrival processes (session and connection arrivals, FTP data connection arrivals within FTP sessions, and TELNET packet arrivals) to determine the error introduced by modeling them using Poisson processes. We find that userinitiated TCP session arrivals, such as remotelogin and filetransfer, are wellmodeled as Poisson processes with fixed hourly rates, but that other connection arrivals deviate considerably from Poisson; that modeling TELNET packet interarrivals as exponential grievously underestimates the burstiness of TELNET traffic, but using the empirical Tcplib [Danzig et al, 1992] interarrivals preserves burstiness over many time scales; and that FTP data connection arrivals within FTP sessions come bunched into “connection bursts,” the largest of which are so large that they completely dominate FTP data traffic. Finally, we offer some results regarding how our findings relate to the possible selfsimilarity of widearea traffic.
Fast Approximation of SelfSimilar Network Traffic
, 1995
"... Recent network traffic studies argue that network arrival processes are much more faithfully modeled using statistically selfsimilar processes instead of traditional Poisson processes [LTWW94a, PF94]. One difficulty in dealing with selfsimilar models is how to efficiently synthesize traces (sample ..."
Abstract

Cited by 107 (0 self)
 Add to MetaCart
(Show Context)
Recent network traffic studies argue that network arrival processes are much more faithfully modeled using statistically selfsimilar processes instead of traditional Poisson processes [LTWW94a, PF94]. One difficulty in dealing with selfsimilar models is how to efficiently synthesize traces (sample paths) corresponding to selfsimilar traffic. We present a fast Fourier transform method for synthesizing approximate selfsimilar sample paths and assess its performance and validity. We find that the method is as fast or faster than existing methods and appears to generate a closer approximation to true selfsimilar sample paths than the other known fast method (Random Midpoint Displacement). We then discuss issues in using such synthesized sample paths for simulating network traffic, and how an approximation used by our method can dramatically speed up evaluation of Whittle's estimator for H, the Hurst parameter giving the strength of longrange dependence present in a selfsimilar time s...
Fast, Approximate Synthesis of Fractional Gaussian Noise for Generating SelfSimilar Network Traffic
 ACM SIGCOMM, Computer Communication Review
, 1997
"... Recent network traffic studies argue that network arrival processes are much more faithfully modeled using statistically selfsimilar processes instead of traditional Poisson processes [LTWW94, PF95]. One difficulty in dealing with selfsimilar models is how to efficiently synthesize traces (sample p ..."
Abstract

Cited by 77 (2 self)
 Add to MetaCart
(Show Context)
Recent network traffic studies argue that network arrival processes are much more faithfully modeled using statistically selfsimilar processes instead of traditional Poisson processes [LTWW94, PF95]. One difficulty in dealing with selfsimilar models is how to efficiently synthesize traces (sample paths) corresponding to selfsimilar traffic. We present a fast Fourier transform method for synthesizing approximate selfsimilar sample paths for one type of selfsimilar process, Fractional Gaussian Noise, and assess its performance and validity. We find that the method is as fast or faster than existing methods and appears to generate close approximations to true selfsimilar sample paths. We also discuss issues in using such synthesized sample paths for simulating network traffic, and how an approximation used by our method can dramatically speed up evaluation of Whittle's estimator for H, the Hurst parameter giving the strength of longrange dependence present in a selfsimilar time series. 1
A Seasonal Periodic Long Memory Model for Monthly River Flows
, 1998
"... Based on simple time series plots and periodic sample autocorrelations, we document that monthly river flow data display long memory, in addition to pronounced seasonality. In fact, it appears that the long memory characteristics vary with the season. To describe these two properties jointly, we ..."
Abstract

Cited by 14 (3 self)
 Add to MetaCart
Based on simple time series plots and periodic sample autocorrelations, we document that monthly river flow data display long memory, in addition to pronounced seasonality. In fact, it appears that the long memory characteristics vary with the season. To describe these two properties jointly, we propose a seasonal periodic long memory model and fit it to the wellknown Fraser river data (to be obtained from Statlib at http://lib.stat.cmu.edu/datasets/). We provide a statistical analysis and provide impulse response functions to show that shocks in certain months of the year have a longer lasting impact than those in other months. Keywords Seasonal difference, Periodic model, Long Memory, PARFIMA, SPARFIMA 1 Introduction It is well known since the early work by Hurst on Nile data that river flows show persistent fluctuations which may be characterized by long memory. Additional to long memory, most river flow data display pronounced seasonality, both in mean and in variance. ...
Frequency domain empirical likelihood for short and longrange dependent processes
, 2002
"... This paper introduces a version of empirical likelihood based on the periodogram and spectral estimating equations. This formulation handles dependent data through a data transformation (i.e., a Fourier transform) and is developed in terms of the spectral distribution rather than a time domain proba ..."
Abstract

Cited by 9 (2 self)
 Add to MetaCart
(Show Context)
This paper introduces a version of empirical likelihood based on the periodogram and spectral estimating equations. This formulation handles dependent data through a data transformation (i.e., a Fourier transform) and is developed in terms of the spectral distribution rather than a time domain probability distribution. The asymptotic properties of frequency domain empirical likelihood are studied for linear time processes exhibiting both short and longrange dependence. The method results in likelihood ratios which can be used to build nonparametric, asymptotically correct confidence regions for a class of normalized (or ratio) spectral parameters, including autocorrelations. Maximum empirical likelihood estimators are possible, as well as tests of spectral moment conditions. The methodology can be applied to several inference problems such as Whittle estimation and goodnessoffit testing. 1. Introduction. The
Traffic Modeling; Prediction, and Congestion Control for HighSpeed Networks: A Fuzzy AR Approach
 IEEE Trans, on FS
, 2000
"... Abstract—In general, highspeed network traffic is a complex, nonlinear, nonstationary process and is significantly affected by immeasurable parameters and variables. Thus, a precise model of this process becomes increasingly difficult as the complexity of the process increases. Recently, fuzzy mode ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
(Show Context)
Abstract—In general, highspeed network traffic is a complex, nonlinear, nonstationary process and is significantly affected by immeasurable parameters and variables. Thus, a precise model of this process becomes increasingly difficult as the complexity of the process increases. Recently, fuzzy modeling has been found to be a powerful method to effectively describe a real, complex, and unknown process with nonlinear and timevarying properties. In this study, a fuzzy autoregressive (fuzzyAR) model is proposed to describe the traffic characteristics of highspeed networks. The fuzzyAR model approximates a nonlinear timevariant process with a combination of several linear local AR processes using a fuzzy clustering method. We propose that the use of this fuzzyAR model has greater potential for congestion control of packet network traffic. The parameter estimation problem in fuzzyAR modeling is treated by a clustering algorithm developed from actual traffic data in highspeed networks. Based on adaptive ARprediction model and queueing theory, a simple congestion control scheme is proposed to provide an efficient traffic management for highspeed networks. Finally, using the actual ethernetLAN packet traffic data, several examples are given to demonstrate the validity of this proposed method for highspeed network traffic control. Index Terms—Cell loss rate, fuzzyAR approach, quality of service (QoS), traffic prediction. I.
Testing the martingale hypothesis
 Eds.), Palgrave Handbook of Econometrics. Palgrave MacMillan
"... This article examines testing the Martingale Di¤erence Hypothesis (MDH) and related statistical inference issues. The earlier literature on testing the MDH was based on linear measures of dependence, such as sample autocorrelations, for instance the classical BoxPierce Portmanteau test and the Va ..."
Abstract

Cited by 5 (0 self)
 Add to MetaCart
This article examines testing the Martingale Di¤erence Hypothesis (MDH) and related statistical inference issues. The earlier literature on testing the MDH was based on linear measures of dependence, such as sample autocorrelations, for instance the classical BoxPierce Portmanteau test and the Variance Ratio test. In order to account for the existing nonlinearity in economic and
nancial data, two directions have been entertained. First, to modify these classical approaches by taking into account the possible nonlinear dependence. Second, to use more sophisticated statistical tools such as those based on empirical processes theory or the use of generalized spectral analysis. This paper discusses these developments and applies them to exchange rate data.
The Empirical Properties of Some Popular Estimators of Long Memory Processes, Working Paper 13/2008
, 2008
"... ..."
MeanShifts and LongMemory in the U.S. ExPost Real Interest Rate
, 2004
"... We fit structural change and longmemory models to examine the quarterly structure of the U.S. expost real interest rate. The results are of substantial importance and indicate that the series shows real breaks since the structural change effect is not totally due to the longmemory property. On th ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
We fit structural change and longmemory models to examine the quarterly structure of the U.S. expost real interest rate. The results are of substantial importance and indicate that the series shows real breaks since the structural change effect is not totally due to the longmemory property. On the other hand, it shows spurious longmemory since this feature is totally explained by the presence of breaks in the series rather than an I (d) process. Our results partially contradict those of Boutahar and Jouini (2004) who find that the U.S. inflation rate shows real breaks and longmemory. Specification tests and maximum likelihood estimates support the fitted models.