The Influencing Factors of Network Packet Loss’s Long-Range Dependence has Impacts on the Packet Loss Rate

Jin Wang 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.11

In order to better establish no-reference video quality assessment model considering the network packet loss and further gain a better QoE evaluation to meet the needs of the user’s, so we build NS2+ MyEvalvid simulation platform to study the scale characteristic of the network packet loss, scale characteristic of packet loss through the influence of packet loss rate to influence QoE. The experimental results show that, packet loss processes has long-range dependence, the number of superimposed source N, shape parameter, Hurst parameter, the output link speed have impacts on long-range dependence. We came to the conclusion that when superimposed source N is more, shape parameter is smaller, Hurst parameter is bigger, the output link speed is smaller, packet loss’s long range dependence is larger, packet loss rate is high.

광역 네트워크 트래픽의 장거리 상관관계와 1/? 노이즈

이창용(Chang-Yong Lee) 한국정보과학회 2010 정보과학회논문지 : 정보통신 Vol.37 No.1

본 논문에서는 네트워크 트래픽의 수동적 측정치 분석을 통해 잘 알려진 장거리 상관관계가 광역 네트워크의 능동적 측정치에도 존재하는지 여부를 관련 분석법을 통하여 검정하고자 한다. 이를 위하여 PingER 프로젝트를 통하여 측정된 광역 네트워크 트래픽의 대표적인 능동적 측정치인 RTT(Round Trip Time)와 RTT의 변동성 시계열 데이터에 대하여 분석을 수행하였다. RTT 시계열 데이터는 장거리 상관관계 혹은 1/? 노이즈의 특성을 보였으며, RTT의 고차원 변화량으로 정의된 변동성은 로그정규분포를 따르며 변동성에 대한 장거리 상관관계는 고려하는 시간 간격이 짧은 경우 장거리 상관관계를 보이고, 시간 간격이 긴 경우에는 장거리 상관관계 혹은 1/? 노이즈를 따름을 밝혔다. 본 연구를 통해 볼 때 장거리 상관관계는 비단 패킷 도착의 시간 간격 등과 같은 수동적 측정뿐만 아니라 RTT와 같은 능동적 측정에서도 나타나는 특징이며, 특히 능동적 측정에는 수동적 측정에는 잘 나타나지 않는 1/? 노이즈 특성이 존재함을 밝혔다. In this paper, we examine a long-range dependence in an active measurement of a network traffic which has been a well known characteristic from analyses of a passive network traffic measurement. To this end, we utilize RTT(Round Trip Time), which is a typical active measurement measured by PingER project, and perform a relevant analysis to a time series of both RTT and its volatilities. The RTT time series exhibits a long-range dependence or a 1/? noise. The volatilities, defined as a higher-order variation, follow a log-normal distribution. Furthermore, volatilities show a long-range dependence in relatively short time intervals, and a long-range dependence and/or 1/? noise in long time intervals. From this study, we find that the long-range dependence is a characteristic of not only a passive traffic measurement but also an active measurement of network traffic such as RTT. From these findings, we can infer that the long-range dependence is a characteristic of network traffic independent of a type of measurements. In particular, an active measurement exhibits a 1/? noise which cannot be usually found in a passive measurement.

광역 네트워크 트래픽의 장거리 상관관계와 노이즈

이창용 한국정보과학회 2010 정보과학회논문지 : 정보통신 Vol.37 No.1

In this paper, we examine a long-range dependence in an active measurement of a network traffic which has been a well known characteristic from analyses of a passive network traffic measurement. To this end, we utilize RTT(Round Trip Time), which is a typical active measurement measured by PingER project, and perform a relevant analysis to a time series of both RTT and its volatilities. The RTT time series exhibits a long-range dependence or a noise. The volatilities, defined as a higher-order variation, follow a log-normal distribution. Furthermore, volatilities show a long-range dependence in relatively short time intervals, and a long-range dependence and/or noise in long time intervals. From this study, we find that the long-range dependence is a characteristic of not only a passive traffic measurement but also an active measurement of network traffic such as RTT. From these findings, we can infer that the long-range dependence is a characteristic of network traffic independent of a type of measurements. In particular, an active measurement exhibits a noise which cannot be usually found in a passive measurement. 본 논문에서는 네트워크 트래픽의 수동적 측정치 분석을 통해 잘 알려진 장거리 상관관계가 광역 네트워크의 능동적 측정치에도 존재하는지 여부를 관련 분석법을 통하여 검정하고자 한다. 이를 위하여 PingER 프로젝트를 통하여 측정된 광역 네트워크 트래픽의 대표적인 능동적 측정치인 RTT(Round Trip Time)와 RTT의 변동성 시계열 데이터에 대하여 분석을 수행하였다. RTT 시계열 데이터는 장거리 상관관계 혹은 노이즈의 특성을 보였으며, RTT의 고차원 변화량으로 정의된 변동성은 로그정규분포를 따르며 변동성에 대한 장거리 상관관계는 고려하는 시간 간격이 짧은 경우 장거리 상관관계를 보이고, 시간 간격이 긴 경우에는 장거리 상관관계 혹은 노이즈를 따름을 밝혔다. 본 연구를 통해 볼 때 장거리 상관관계는 비단 패킷 도착의 시간 간격 등과 같은 수동적 측정뿐만 아니라 RTT와 같은 능동적 측정에서도 나타나는 특징이며, 특히 능동적 측정에는 수동적 측정에는 잘 나타나지 않는 노이즈 특성이 존재함을 밝혔다.

Steady-State Simulation of Long-Range Dependent Queuing Processes

정해덕 한국물리학회 2009 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.55 No.5

Long-range dependence was first noted in hydrology by H. E. Hurst from a study of the water levels of the Nile river which showed a tendency for a flood year to be followed by another flood year. Long-range dependent processes are relevant not only in telecommunication networks but also in such areas of scientific activity as econophysics, climatology, economics, environmental sciences, geology, geophysics, hydrology, computer science, and computer engineering. They provide good models of packet traffic in telecommunication networks, for example, in local area networks and video traffic, and good models of persistence analysis in econophysics, especially for short-term interest rates. In this paper, the effects of long-range dependence on the behaviors of queuing systems have been investigated. We have also investigated how the long-range dependence in arrival processes affects the length of sequential steady-state simulations being executed to obtain simulation results with the required level of statistical error. Our results show that the finite buffer overflow probability of a queuing system with a long-range dependent input is much greater than the equivalent queuing system with a Poisson or a short-range dependent input process and that the overflow probability increases as the Hurst parameter approaches one.

딥러닝을 이용한 이변량 장기종속시계열 예측

김지영,백창룡 한국통계학회 2019 응용통계연구 Vol.32 No.1

We consider bivariate long range dependent (LRD) time series forecasting using a deep learning method. A long short-term memory (LSTM) network well-suited to time series data is applied to forecast bivariate time series; in addition, we compare the forecasting performance with bivariate fractional autoregressive integrated moving average (FARIMA) models. Out-of-sample forecasting errors are compared with various performance measures for functional MRI (fMRI) data and daily realized volatility data. The results show a subtle difference in the predicted values of the FIVARMA model and VARFIMA model. LSTM is computationally demanding due to hyper-parameter selection, but is more stable and the forecasting performance is competitively good to that of parametric long range dependent time series models. 본 논문에서는 딥러닝을 이용한 이변량 장기종속시계열(long-range dependent time series) 예측을 고려하였다. 시계열 데이터 예측에 적합한 LSTM(long short-term memory) 네트워크를 이용하여 이변량 장기종속시계열을 예측하고 이를 이변량 FARIMA(fractional ARIMA) 모형인 FIVARMA 모형과 VARFIMA 모형과의 예측 성능을 실증 자료 분석을 통해 비교하였다. 실증 자료로는 기능적 자기공명 영상(fMRI) 및 일일 실현 변동성(daily realized volatility) 자료를 이용하였으며 표본외 예측(out-of sample forecasting) 오차 비교를 통해 예측 성능을 측정하였다. 그 결과, FIVARMA 모형과 VARFIMA 모형의 예측값에는 미묘한 차이가 존재하며, LSTM 네트워크의 경우 초매개변수 선택으로 복잡해 보이지만 계산적으로 더 안정되면서 예측 성능도 모수적 장기종속시계열과 뒤지지 않은 좋은 예측 성능을 보였다.

A piecewise polynomial trend against long range dependence

백창룡 한국통계학회 2015 Journal of the Korean Statistical Society Vol.44 No.3

A sequential testing procedure to distinguish between a piecewise polynomial trend superimposed by short-range dependence and long range dependence is examined. The proposed procedure is based on the local Whittle estimation of long range dependence parameter from the residual series obtained by removing a piecewise polynomial trend. All results are provided with theoretical justifications, and Monte Carlo simulations show that our method achieves good size and provides reasonable power against long range dependence. The proposed method is illustrated to the historical Northern Hemisphere temperature data.

Frequency domain bootstrap for ratio statistics under long-range dependence

김영민,임종호 한국통계학회 2019 Journal of the Korean Statistical Society Vol.48 No.4

A frequency domain bootstrap (FDB) is a common technique to apply Efron’s independent and identically distributed resampling technique (Efron, 1979) to periodogram ordinates – especially normalized periodogram ordinates – by using spectral density estimates. The FDB method is applicable to several classes of statistics, such as estimators of the normalized spectral mean, the autocorrelation (but not autocovariance), the normalized spectral density function, and Whittle parameters. While this FDB method has been extensively studied with respect to short-range dependent time processes, there is a dearth of research on its use with long-range dependent time processes. Therefore, we propose an FDB methodology for ratio statistics under long-range dependence, using semi- and nonparametric spectral density estimates as a normalizing factor. It is shown that the FDB approximation allows for valid distribution estimation for a broad class of stationary, long-range (or short-range) dependent linear processes, without any stringent assumptions on the distribution of the underlying process. The results of a large simulation study show that the FDB approximation using a semi- or nonparametric spectral density estimator is often robust for various values of a long-memory parameter reflecting magnitude of dependence. We apply the proposed procedure to two data examples.

자기유사적인 데이터 트래픽 특성을 고려한 대역폭 할당

임석구 한국콘텐츠학회 2005 한국콘텐츠학회논문지 Vol.5 No.3

Recent measurements of local-area and wide-area traffic have shown that network traffic exhibits at a wide range of scales-Self-similarity. Self-similarity is expressed by long term dependency, this is contradictory concept with Poisson model that have relativity short term dependency. Therefore, first of all for design and dimensioning of next generation communication network, traffic model that are reflected burstness and self-similarity is required. Here self-similarity can be characterized by Hurst parameter. In this paper, when different many data traffic being integrated under various environments is arrived to communication network, Hurst Parameter's change is analyzed and compared with simulation results. 현재 제공되는 인터넷 서비스들의 동작 특성은 기존에 고려되던 트래픽 특성과는 완전히 다른 자기 유사성(Self-similarity)이라는 성질을 가진다는 것이 증명되었다. 자기 유사성은 장기간 의존성으로 표현되는데, 이것은 단기간 의존성 성질을 갖는 기존의 모델인 포아송(Poisson) 모델과는 상반되는 개념이다. 따라서 차세대 통신망의 설계 및 디멘져닝을 위해서는 무엇보다도 데이터 트래픽의 주요 특성인 버스트성(Burstiness)과 자기유사성이 반영된 트래픽 모델이 요구된다. 여기서 자기유사성은 허스트 파라미터(Hurst Parameter)로 특성화 될 수 있다. 이러한 관점에서 본 논문에서는 데이터 트래픽 특성이 서로 다른 다수의 데이터 트래픽의 통합되어 통신망에 입력되는 경우 주요 파라미터인 허스트 파라미터의 변화를 다양한 환경 하에서 분석하였고 이를 시뮬레이션 결과와도 비교하였다.

Bootstrap-Based Test for Volatility Shifts in GARCH against Long-Range Dependence

Wang, Yu,Park, Cheolwoo,Lee, Taewook The Korean Statistical Society 2015 Communications for statistical applications and me Vol.22 No.5

Volatility is a variation measure in finance for returns of a financial instrument over time. GARCH models have been a popular tool to analyze volatility of financial time series data since Bollerslev (1986) and it is said that volatility is highly persistent when the sum of the estimated coefficients of the squared lagged returns and the lagged conditional variance terms in GARCH models is close to 1. Regarding persistence, numerous methods have been proposed to test if such persistency is due to volatility shifts in the market or natural fluctuation explained by stationary long-range dependence (LRD). Recently, Lee et al. (2015) proposed a residual-based cumulative sum (CUSUM) test statistic to test volatility shifts in GARCH models against LRD. We propose a bootstrap-based approach for the residual-based test and compare the sizes and powers of our bootstrap-based CUSUM test with the one in Lee et al. (2015) through simulation studies.

CENTRAL LIMIT THEOREMS FOR A VECTOR PROCESSES OF NON-LINEAR FUNCTIONALS OF GAUSSIAN SEQUENCES OF VECTORS

Jeon, Tae-Il Korean Mathematical Society 1995 대한수학회논문집 Vol.10 No.4

We formulate central limit theorems for non-linear functionals of stationary Gaussian vector processes with long-range dependence.