A. Sang and S.-Q. Li. A predictability analysis of network traffic. In INFOCOM (1), pages 342--351, 2000. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
Netwoer Traffic Analysis, Classification, and Prediction - Qiao, Dinda (2003) (Correct)
....increases with increasing bin size. However, such smoothing often does not monotonically increase predictability. About half of the AUCKLAND traces exhibited a sweet spot, a degree of smoothing at which predictability is maximized, contradicting the work of an earlier prediction study [11]. We believe that our classification scheme and representative traces will be helpful to ourselves and others in the pursuit of techniques to predict network behavior. Hence we have reported on it in considerable depth, including an appendix. Our prediction study is a first step that we are ....
SANG, A., AND LI, S. Predictability analysis of network traffic. In Proceedings of INFOCOM 2000 (2000), pp. 342--351.
On Exploiting Long Range Dependence of Network Traffic in.. - Guanghui He And (2003) (2 citations) (Correct)
....coefficients and can be expressed as a 1 a 2 . R(n) R(n # # . R(n 1) R(n # 1 , 10) where R(n) is the covariance function of the time series, and can be estimated in practice as R(i) # = R (m) i) nn t=i 1f (t)f a (t i) 11) Sang et al. [24] has shown that the above estimator can make very good prediction of a (n 1) After a (n 1) is predicted, a (n 2) can be predicted by usingf a (k) k = 1, 2, n and a (n 1) a (n 3) can be predicted by using a (k) k=1, 2, n, a (n 1) and a (n 2) and so on. The ....
A. Sang, and S.-q. Li. A predictability analysis of network traffic, Proc. of IEEE INFOCOM'2000.
The Influence of Long-range Dependence on Prediction - Östring, Sirisena (2001) (Correct)
....architecture, like in ATM networks [3] 4] or in the IP DiitServ framework [5] Simple, high performance predictors are required that are computationally efficient. Analysing the performance of prediction techniques and the predictability of network traffic is an important study in its own right [6]. The quality of a prediction depends on the amount of uncertainity that accompanies the prediction, and mathematically this is measured by the variance of the prediction error. This uncertainity depends on a number of factors, including the amount of traffic history that is used to make the ....
A. Sang and San-qi Li, "A predictability analysis of network traffic," Proceedings of INFOCOM '00, pp. 342-351, Mar. 2000.
Multiscale Predictability of Network Traffic - Qiao, Skicewicz, Dinda (2002) (2 citations) (Correct)
.... prediction and found that running multiple predictors (mean, median, and AR models) simultaneously and forecasting with the one currently exhibiting the smallest prediction error produced the best results on his measurements [35] Closest to the work described in this paper is that of Sang and Li [31], who analyzed the prospects for multi step prediction of network traffic using ARMA and MMPP models. Their analysis is based on continuous time ARMA and MMPP models driven by Gaussian noise sources. Making the assumption that such models are appropriate, they then developed analytic expressions ....
SANG, A., AND LI, S. Predictability analysis of network traffic. In Proceedings of INFOCOM 2000 (2000), pp. 342--351.
On Modeling Round-Trip Time Dynamics of the Internet.. - Ohsaki, Morita, Murata (2002) (Correct)
....using system identification. Refer to [7] for the detail of the ARX model. Our approach of a black box modeling using the ARX model is distinctive from other black box approaches, which model network traffic using the AR (Auto Regressive) model or the ARMA (Auto Regressive Moving Average) model [13, 17, 18]. Figure 3 illustrates a typical usage of the AR model or the ARMA model for modeling network traffic. Comparing Figs. 2 and 3, the ARX model has the input whereas either the AR model or the ARMA model does not. In other words, only the ARX model can represent the dynamics, i.e. how the past ....
A. Sang and S.-Q. Li, "A predictability analysis of network traffic," in Proceedings of IEEE INFOCOM 2000, pp. 342--351, Mar. 2000.
On linear prediction of Internet traffic for packet .. - Morato, Aracil.. (2001) (1 citation) (Correct)
....of the process at time # # # . The choice of a prediction method is a tradeoff between the prediction interval, prediction error and computational cost. For video and network data traffic, linear prediction methods have been considered in the literature as a simple and effective alternative [3] [4], 5] Let ### ######## ##### # ######### # ## #####, # ### ####### # with # # ## . The linear predictor that minimizes the variance of the prediction error ### #### # ### ###is given by [6, chapter 1] ## # ## ###### (1) # # # ###### # ### # ## # ### # # # ### # # ##### where # is the ....
.... RESULTS In this section we analyze packet and burst switching networks with sources incorporating the following traffic predictors: Best mean square error linear predictor: This is the predictor in 1, which provides a minimum on the square error and has been adopted in other papers [3] 5] [4]. While this is the best linear predictor we note that non linear predictors, for instance wavelet based [3] which are tailored to the specific case of long range dependent processes, can provide further improvements in prediction error. Ideal predictor (no estimation error) This is a ....
[Article contains additional citation context not shown here]
Aimin Sang and San-qi Li. A predictability analysis of network traffic. In Proceedings of Infocom 2000.
IST Project ATRIUM - Report I4.2: Analysis of Interdomain Traffic - Uhlig, al. (2001) (Correct)
....On one side, most of the traffic prediction techniques proposed so far in the literature did cope with audio and video traffic streams and relied on a statistical model. DGG 99] has evaluated the use of statistical prediction techniques in the context of dynamic VPN provisioning while [SL00] discussed the problem of predictability in network traffic with ARMA and MMPP (Markov Modulated Poisson Processes) models. On the other side, mechanisms for bandwidth reservations based on a leaky bucket model have been proposed but not evaluated on actual traffic. This chapter is aimed at ....
A. Sang and S. Li. A predictability analysis of network traffic. In Proc. IEEE INFOCOM 2000, March 2000.
Predicting Internet End-to-End Delay: An Overview - Ming Yang Rong Self-citation (Li) (Correct)
No context found.
A. Sang and S.-Q. Li. A predictability analysis of network traffic. In INFOCOM (1), pages 342--351, 2000.
Long-Term Forecasting of Internet Backbone Traffic - Papagiannaki, Taft, Zhang, Diot (2003) (5 citations) Self-citation (Li) (Correct)
No context found.
A. Sang and S. Li, "A Predictability Analysis of Network Traffic," in INFOCOM, Tel Aviv, Israel, Mar. 2000.
Long-Term Forecasting of Internet Backbone Traffic.. - Papagiannaki, Taft.. (2003) (5 citations) Self-citation (Li) (Correct)
No context found.
A. Sang and S. Li, "A Predictability Analysis of Network Traffic," in INFOCOM, Tel Aviv, Israel, Mar. 2000.
Long-Term Forecasting of Internet Backbone Traffic.. - Papagiannaki, Taft.. (2003) (5 citations) Self-citation (Li) (Correct)
....address this issue, mentioning that initial attempts toward this direction did not prove fruitful. Other work in the domain of Internet traffic forecasting typically addresses small time scales, such as seconds or minutes, that are relevant for dynamic resource allocation [3] 4] 5] 6] [7], 8] To the best of our knowledge, our work is the first to model the evolution of IP backbone traffic at large time scales, and to develop models for long term forecasting that can be used for capacity planning purposes. III. OBJECTIVES The capacity planning process consists of many tasks, ....
A. Sang and S. Li, "A Predictability Analysis of Network Traffic," in INFOCOM, Tel Aviv, Israel, Mar. 2000.
On the Predictability of Large Transfer TCP - He, Dovrolis, Ammar (2005) (Correct)
No context found.
A. Sang and S. Li. A Predictability Analysis of Network Traffic. Computer Networks, 39(4):329 -- 345, Jul 2002.
IST Project ATRIUM - Report I4.2 Analysis of Interdomain.. - Uhlig, Bonaventure (2001) (Correct)
No context found.
A. Sang and S. Li. A predictability analysis of network traffic. In Proc. IEEE INFOCOM 2000, March 2000.
On the Influence of Self-similarity on Optical Burst.. - Izal And Aracil (Correct)
No context found.
A. Sang and S. Li, "A predictability analysis of network traffic," in Proceedings of Infocom 2000.
Multiscale Predictability of Network Traffic - Qiao, Skicewicz, Dinda (2004) (2 citations) (Correct)
No context found.
SANG, A., AND LI, S. Predictability analysis of network traffic. In Proceedings of INFOCOM 2000 (2000), pp. 342--351.
Distortion-Buffer Optimized TCP Video Streaming - Sehgal, Verscheure, Frossard (2004) (Correct)
No context found.
A.Sang, San-qi Li, "A Predictability Analysis of Network Traffic," IEEE INFOCOM 2000, Tel-Aviv, Israel.
Provisioning IP Backbone Networks Based on Measurements - Papagiannaki (2003) (Correct)
No context found.
A. Sang and S. Li, "A Predictability Analysis of Network Traffic," in INFOCOM, Tel Aviv, Israel, Mar. 2000.
Modeling End-to-End Packet Delay Dynamics of the Internet - Using System Identification (2000) (Correct)
No context found.
A. Sang and S.-Q. Li, "A predictability analysis of network traffic," in Proceedings of IEEE INFOCOM 2000.
An Empirical Study of the Multiscale Predictability of Network .. - Yi Qiao Jason (2003) (1 citation) (Correct)
No context found.
SANG, A., AND LI, S. Predictability analysis of network traffic. In Proceedings of INFOCOM 2000 (2000), pp. 342--351.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC