Results 1  10
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21
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 ..."
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Cited by 1442 (21 self)
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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 ..."
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Cited by 94 (0 self)
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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 ..."
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Cited by 62 (2 self)
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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 ..."
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Cited by 9 (2 self)
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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. ...
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 ..."
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Cited by 5 (0 self)
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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.
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 ..."
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Cited by 2 (1 self)
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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.
Some New Statistical Approaches to the Analysis of Long Memory Processes
, 1994
"... This thesis describes methods of analysis and synthesis of long memory processes. Long memory processes are those which exhibit correlations between events separated by a long period of time. This phenomenon is characterized in the frequency domain by a sharp peak in the spectral density function as ..."
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Cited by 2 (0 self)
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This thesis describes methods of analysis and synthesis of long memory processes. Long memory processes are those which exhibit correlations between events separated by a long period of time. This phenomenon is characterized in the frequency domain by a sharp peak in the spectral density function as the frequency approaches zero. This characteristic is observed in many physical time series, including those in the fields of geophysics, astronomy and finance. A class of models that captures such long memory behaviour are fractionally differenced processes, the simplest of these processes is obtained by differencing white noise a fractional number of times. We employ two methods of analyzing such processes: Multitaper spectral estimation and Wavelet analysis. Multitaper spectral analysis uses the average of several direct spectral estimators evaluated using orthogonal tapers. We look at two sets of tapers: the discrete prolate spheroidal sequences and sinusoidal tapers. This method of s...
A generalized portmanteau goodnessoffit test for time series models
, 2000
"... We present a goodness of fit test for time series models based on the discrete spectral average estimator. Unlike current tests of goodness of fit, the asymptotic distribution of our test statistic allows the null hypothesis to be either a short or long range dependence model. Our test is in the fre ..."
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Cited by 2 (1 self)
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We present a goodness of fit test for time series models based on the discrete spectral average estimator. Unlike current tests of goodness of fit, the asymptotic distribution of our test statistic allows the null hypothesis to be either a short or long range dependence model. Our test is in the frequency domain, is easy to compute and does not require the calculation of residuals from the fitted model. This is especially advantageous when the fitted model is not a finite order autoregressive model. The test statistic is a frequency domain analogue of the test by Hong (1996) which is a generalization of the BoxPierce (1970) test statistic. A simulation study shows that our test has power comparable to that of Hong's test and superior to that of another frequency domain test by Milhoj (1981).