Results 1  10
of
17
Understanding the limitations of estimation methods for longrange dependence
"... Over the last ten years, longrange dependence (LRD) has become a key concept in modeling networking phenomena. The research community has undergone a mental shift from Poisson and memoryless processes to LRD and bursty processes. Despite its popularity, LRD analysis is hindered by two main problems ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
(Show Context)
Over the last ten years, longrange dependence (LRD) has become a key concept in modeling networking phenomena. The research community has undergone a mental shift from Poisson and memoryless processes to LRD and bursty processes. Despite its popularity, LRD analysis is hindered by two main problems: a) it cannot be used by nonexperts easily, and b) the identification of LRD is often questioned and disputed. The main cause for both these problems is the absence of a systematic and unambiguous way to identify the existence of LRD. This paper has two main thrusts. First, we explore the (lack of) accuracy and robustness in LRD estimation. We find that the current estimation methods can often be inaccurate and unreliable, reporting LRD erroneously. We search for the source of such problems and identify a number of caveats and common mistakes. For example, some of the methods misinterpret shortrange correlations for LRD. Second, we develop methods to improve the robustness of the estimation. Through case studies, we demonstrate the effectiveness of our methods in overcoming most known caveats. Finally, we integrate all required functionality and methods in an easy to use software tool. Our work is a first step towards a systematic approach and a comprehensive tool for the reliable estimation of LRD. I.
On Fractional Tempered Stable Motion
, 2006
"... Fractional tempered stable motion (fTSm) is defined and studied. FTSm has the same covariance structure as fractional Brownian motion, while having tails heavier than Gaussian ones but lighter than (nonGaussian) stable ones. Moreover, in short time it is close to fractional stable Lévy motion, whil ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Fractional tempered stable motion (fTSm) is defined and studied. FTSm has the same covariance structure as fractional Brownian motion, while having tails heavier than Gaussian ones but lighter than (nonGaussian) stable ones. Moreover, in short time it is close to fractional stable Lévy motion, while it is approximately fractional Brownian motion in long time. A series representation of fTSm is derived and used for simulation and to study some of its sample path properties.
HURST PARAMETER ESTIMATION FOR EPILEPTIC SEIZURE DETECTION
"... Abstract. Estimation of the Hurst parameter provides information about the memory range or correlations (long vs. short) of processes (timeseries). A new application for the Hurst parameter, realtime event detection, is identified. Hurst estimates using rescaled range, dispersional and bridgedetr ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract. Estimation of the Hurst parameter provides information about the memory range or correlations (long vs. short) of processes (timeseries). A new application for the Hurst parameter, realtime event detection, is identified. Hurst estimates using rescaled range, dispersional and bridgedetrended scaled windowed variance analyses of seizure timeseries recorded from human subjects reliably detect their onset, termination and intensity. Detection sensitivity is unaltered by signal decimation and window size increases. The high sensitivity to brain state changes, ability to operate in real time and small computational requirements make Hurst parameter estimation using any of these three methods well suited for implementation into miniature implantable devices for contingent delivery of antiseizure therapies.
Network Traffic Model for Industrial Environment
"... Abstract—In this paper, a model of LAN traffic is presented. In the model, the most important components influencing the network traffic are taken into account. Namely, the transmission protocols and information buffering, operating systems, and queuing algorithms as well as user behavior in networ ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Abstract—In this paper, a model of LAN traffic is presented. In the model, the most important components influencing the network traffic are taken into account. Namely, the transmission protocols and information buffering, operating systems, and queuing algorithms as well as user behavior in network applications are considered. The model is based on an “onoff ” function. The network traffic observed at the physical layer is a superposition of many sequential and selfsimilar “onoff ” processes. It has been shown that the selfsimilarity of the traffic, measured by the Hurst parameter, changes from almost 1.0 for very low frequencies to 0.5 for high frequencies. Index Terms—Components traffic, network protocols, traffic model. I.
Applicability of Different Models of Burstiness to Energy Consumption Estimation
"... Abstract The advent of internet use through mobile devices using WLAN has pushed the research of energy consumption quite aggressively to find a better solution that will lead to a longer battery life. There are quite a few ways to reduce the energy consumption of a mobile device e.g. rate adaptati ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract The advent of internet use through mobile devices using WLAN has pushed the research of energy consumption quite aggressively to find a better solution that will lead to a longer battery life. There are quite a few ways to reduce the energy consumption of a mobile device e.g. rate adaptation, sleeping, battery recovery effect, different queuing algorithms etc. Most of these techniques deals with by adapting some method at the device end. However, in this paper we will look at the power consumption issue from the network or data source point of view. We will first show what kind of network traffic causes a reduced power consumption in the mobile device and then we will approach for a traffic model that fits best to that kind of traffic. Through our experiment we have found that bursty traffic consumes less power in the mobile device compared to the smooth data traffic. Having figuring out the traffic nature, we have used an On/Off model to capture the burstiness of data traffic. Our aim here is to model this bursty traffic using traffic modelling techniques so that the model can be used in data sources in the internet to send data traffic to the mobile devices in such a way that the energy consumption will be reduced.
CoLoRaDe: A Novel Algorithm for Controlling Longrange Dependent Network Traffic
"... Abstract Longrange dependence characteristics have been observed in many natural or physical phenomena. In particular, a significant impact on data network performance has been shown in several papers. Congested Internet situations, where TCP/IP buffers start to fill, show longrange dependent (LR ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract Longrange dependence characteristics have been observed in many natural or physical phenomena. In particular, a significant impact on data network performance has been shown in several papers. Congested Internet situations, where TCP/IP buffers start to fill, show longrange dependent (LRD) selfsimilar chaotic behaviour. The exponential growth of the number of servers, as well as the number of users, causes the performance of the Internet to be problematic since the LRD traffic has a significant impact on the buffer requirements. The Internet is a largescale, widearea network for which the importance of measurement and analysis of traffic is vital. The intensity of the longrange dependence (LRD) of communications network traffic can be measured using the Hurst parameter. A variety of techniques (such as R/S analysis, aggregated variancetime analysis, periodogram analysis, Whittle estimator, Higuchi’s method, Waveletbased estimator, absolute moment method, etc.) exist for estimating Hurst exponent but the accuracy of the estimation is still a complicated and controversial issue. Earlier research [1] introduced a novel estimator called the Hurst Exponent from the Autocorrelation Function (HEAF) and it was shown why lag 2 in HEAF (i.e. HEAF (2)) is considered when estimating LRD of network traffic. HEAF estimates H by a process which is simple, quick and reliable. In this research we extend these concepts by introducing a novel algorithm for controlling the longrange dependence of network traffic, named CoLoRaDe which is shown to reduce the LRD of packet sequences at the router buffer. I.
The Human’s Behaviour influence on the traffic in LAN
"... In the paper the users ’ behaviour and the network applications influence on the level and shape of the LAN traffic, which is the selfsimilar in a wide range of frequency have been analyzed. The description was done by using the “onoff ” model. The aims of the traffic models presented so far is th ..."
Abstract
 Add to MetaCart
In the paper the users ’ behaviour and the network applications influence on the level and shape of the LAN traffic, which is the selfsimilar in a wide range of frequency have been analyzed. The description was done by using the “onoff ” model. The aims of the traffic models presented so far is the approximation or possibly the description of the traffic in physical layer, which is a superposition of many sequential and asynchronous processes. In this paper the characteristics of loading link in LAN, which show the dominated users ’ behaviour influence in the low frequency traffic components has been presented. The conclusions concerning the usage of the traffic model and the influence of the users ’ behaviour to the description and optimalization of the transmission track were carried out. 1.
Estimation PeertoPeer Network’s BurstyTraffic Parameters
"... Abstract: Data traffic traces are known to be bursty with long range dependence, due to large and wildly fluctuating file sizes of data transfers. This is especially true in Peertopeer network data traffic. The exact selfsimilarity model of long range dependence can pose analytical and practical ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract: Data traffic traces are known to be bursty with long range dependence, due to large and wildly fluctuating file sizes of data transfers. This is especially true in Peertopeer network data traffic. The exact selfsimilarity model of long range dependence can pose analytical and practical problems at very small and very large time lags. In our model, the time series of the traffic trace (referred to as the signal) is assumed to possess an autocovariance profile corresponding to exact selfsimilarity over a range of lags, {k}, satisfying M < k < L. At lower lags, exact selfsimilarity may breakdown, or additive moving average type noise (inaccuracies) may corrupt the autocovariances. At very high lags, far beyond the number of observed samples, the autocovariance structure is irrelevant and may be assumed to be infinite summable. Therefore, L can be as large as desired. Applications of such a model are discussed. The mean, variance, and the Hurst parameter of the signal, as well as the autocovariances of any independent zero mean moving average type additive noise are assumed to be unknown. A class of linear combinations of sample average second order statistics of noisy observations is constructed. They are unbiased estimates of their corresponding expectations. These expectations are shown to be devoid of the noise statistics. The ratio of two such expectations eliminates the signal variance. The ratio is a well behaved monotonic function of the only remaining unknown, the Hurst parameter. Equating the ratio of these expectations to the ratio of the corresponding sample averages from the noisy observations leads to a very easily solvable nonlinear equation with a unique root. The other parameters of bursty traffic are easily estimated with the help of the estimated Hurst parameter. The result and related issues are discussed. KeyWords: Peertopeer network traffic, Traffic modeling and analysis, Selfsimilarity, Hurst parameter, and Parameter estimation.
Estimation of ordinal pattern probabilities in fractional Brownian motion
, 2008
"... For equidistant discretizations of fractional Brownian motion (fBm), the probabilities of ordinal patterns of order d = 2 are monotonically related to the Hurst parameter H. By plugging the sample relative frequency of those patterns indicating changes between up and down into the monotonic relation ..."
Abstract
 Add to MetaCart
(Show Context)
For equidistant discretizations of fractional Brownian motion (fBm), the probabilities of ordinal patterns of order d = 2 are monotonically related to the Hurst parameter H. By plugging the sample relative frequency of those patterns indicating changes between up and down into the monotonic relation to H, one obtains the Zero Crossing (ZC) estimator of the Hurst parameter which has found considerable attention in mathematical and applied research. In this paper, we generally discuss the estimation of ordinal pattern probabilities in fBm. As it turns out, according to the sufficiency principle, for ordinal patterns of order d = 2 any reasonable estimator is an affine functional of the sample relative frequency of changes. We establish strong consistency of the estimators and show them to be asymptotically normal for H < 3 4. Further, we derive confidence intervals for the Hurst parameter. Simulation studies show that the ZC estimator has larger variance but less bias than the HEAF estimator of the Hurst parameter.
Robustness of HEAF(2) for Estimating the Intensity of Longrange Dependent Network Traffic
, 2006
"... The Intensity Of Longrange Dependence (LRD) for communications network traffic can be measured using the Hurst parameter. LRD characteristics in computer networks, however, present a fundamentally different set of problems in research towards the future of network design. There are various estimato ..."
Abstract
 Add to MetaCart
The Intensity Of Longrange Dependence (LRD) for communications network traffic can be measured using the Hurst parameter. LRD characteristics in computer networks, however, present a fundamentally different set of problems in research towards the future of network design. There are various estimators of the Hurst parameter, which differ in the reliability of their results. Getting robust and reliable estimators can help to improve traffic characterization, performance modelling, planning and engineering of real networks. Earlier research [1] introduced an estimator called the Hurst Exponent from the Autocorrelation Function (HEAF) and it was shown why lag 2 in HEAF (i.e. HEAF (2)) is considered when estimating LRD of network traffic. This paper considers the robustness of HEAF(2) when estimating the Hurst parameter of data traffic (e.g. packet sequences) with outliers.