K. Park and W. Willinger, "Self-similar network traffic: An overview," in Self-Similar Network Traffic and Performance Evaluation, K. Park and W. Willinger, Eds. New York: Wiley, 2000, pp. 1--38.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....analysis, log normal distribution, loss probability I. INTRODUCTION CALnqG and multi scale behaviors, such as the long range dependence, the self similarity, and the multi fractality, have been commonly viewed as the most significant characteristics of the Internet traffic today [11] 13] 18] [19] [20] They are found not only in the wired networks but also in the wireless networks [21] 29] the Ad hoc networks [12] and the satellite networks [26] These behaviors generally mean that the traffic is bursty in many time scales and among many orders of statistics. They make the network ....

IC Park and W. Willinger, "Self-similar network traffic: an overview," In SelSimilar Network Traffic and Performance Evaluation, WileyInterscience, 2000.

....times) will occur from time to time. This is one of the reasons why analytic models employing traditional negative exponential distribution often provide overly optimistic estimates for the delays and queue sizes the probability of an extreme event is negligible. We refer the reader to [9] and [10] for a more rigorous treatment of self similarity and long range dependence. To generate self similar traffic, we used the method described in [11] where the resulting traffic is an aggregation of multiple streams, each consisting of alternating Pareto distributed ON OFF periods. Pareto ....

....will be shorter. 28 Linear Credit # # i Const V This scheme uses a similar approach as the Constant Credit scheme. However, the size of the credit is proportional to the requested window. The reasoning here is the following: LRD traffic possesses a certain degree of predictability (see [9]) viz. if we observe a long burst of data, then this burst is likely to continue for some time into the future. Correspondingly, the arrival of more data during the last cycle time may signal that we are observing a burst of packets. Elastic # # # # = i N i j j MAX i W NW V ....

K. Park and W. Willinger, Self-similar network traffic: An overview, In K. Park and W. Willinger, editors, Self-Similar Network Traffic and Performance Evaluation. Wiley Interscience, 2000.

....interarrival times and transfer times are long tailed, but that there is some evidence for long tailed burst sizes. We speculate on the causes of long tailed bursts. I. INTRODUCTION Numerous studies have reported traffic patterns in the Internet that show characteristics of self similarity (see [1] for a survey) Many proposed explanations of this phenomenon are based on the assumption that the distribution of transfer times in the network is long tailed [2] 3] 4] 5] In turn, this assumption is based on the assumption that the distribution of file sizes is long tailed [6] 7] In ....

Kihong Park and Walter Willinger, "Self-similar network traffic: An overview," in Self-Similar Network Traffic and Performance Evaluation, chapter 1. Wiley-Interscience, 2000, Preprint.

....conclusion for proposed explanations of self similarity in the Internet. Keywords: File sizes, lognormal distribution, long tailed distribution, self similarity. 1. Introduction Numerous studies have reported traffic patterns in the Internet that show characteristics of self similarity (see [17] for a survey) Most proposed explanations are based on the assumption that the distribution of transfer times in the network is long tailed [19] 18] 22] 12] In turn, this assumption is based on the assumption that the distribution of file sizes is long tailed [16] 9] We contend that the ....

K. Park and W. Willinger. Self-similar network traffic: An overview. In Self-Similar Network Traffic and Performance Evaluation, chapter 1. Wiley-Interscience, 2000. Preprint.

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PARK,K.AND WILLINGER, W. 2000b. Self-similar network traffic: An overview. In Self-Similar Network Traffic and Performance Evaluation, K. Park and W. Willinger, Eds., Wiley-Interscience, New York, 1--38.

....Control (MTSC) and showed its effectiveness at enhancing performance for ratebased feedback control. We showed that by incorporating correlation structure at large time scales into a generic rate based feedback congestion control, we are able to improve performance significantly. In [Tuan and Park 2000], we applied MTSC to the control of real time multimedia traffic in particular, MPEG video using adaptive redundancy control, and we showed that end to end quality of service (QoS) is significantly enhanced by utilizing large time correlation structure in both the background and source ....

....H = 1=2 is self similar but it is not long range dependent. For second order self similarity with H 1=2, however, one implies the other and it is for this reason that we sometimes use the terms interchangably within the traffic modeling context. A more comprehensive discussion can be found in [Park and Willinger 2000b] There is an intimate relationship between heavy tailed distributions and longrange dependence in the networking context in that the former can be viewed as causing the latter [Feldmann et al. 1998; Park et al. 1996; Willinger et al. 1995] We say a random variable Z has a heavy tailed ....

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Park, K. and Willinger, W. 2000b. Self-similar network traffic: An overview. In K. Park and W. Willinger Eds., Self-Similar Network Traffic and Performance Evaluation. Wiley-Interscience.

....motion with H = 1=2 is self similar but it is not long range dependent. For second order self similarity, however, one implies the other and it is for this reason that we sometimes use the terms interchangably within the traffic modeling context. A more comprehensive discussion can be found in [29]. There is an intimate relationship between heavy tailed distributions and long range dependence in the networking context in that the former can be viewed as causing the latter [14, 26] We say a random variable Z has a heavy tailed distribution if PrfZ xg c x Gammaff ; x 1 (2.2) where 0 ....

K. Park and W. Willinger. Self-similar network traffic: An overview. In K. Park and W. Willinger, editors, Self-Similar Network Traffic and Performance Evaluation. Wiley Interscience, 1999.

....motion with H = 1=2 is self similar but it is not long range dependent. For secondorder self similarity, however, one implies the other and it is for this reason that we sometimes use the terms interchangably within the traffic modeling context. A more comprehensive discussion can be found in [24]. B. LRD and Predictability Given X t and X (m) i , we will be interested in estimating PrfX (m) i 1 j X (m) i g for some suitable aggregation level m 1. If X t is short range dependent, we have PrfX (m) i 1 j X (m) i g PrfX (m) i 1 g for large m whereas for long range dependent ....

K. Park and W. Willinger. Self-similar network traffic: An overview. In K. Park and W. Willinger, editors, Self-Similar Network Traffic and Performance Evaluation. Wiley Interscience, 1999.

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K. Park and W. Willinger, "Self-similar network traffic: An overview," in Self-Similar Network Traffic and Performance Evaluation, K. Park and W. Willinger, Eds. New York: Wiley, 2000, pp. 1--38.

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K. Park and W. Willinger. Self-similar network traffic: An Overview. In Self-Similar Network Traffic and Performance Evaluation, chapter Introduction. Wiley-Interscience, NY, 2000.

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K. Park and W. Willinger. Self-similar network traffic: An Overview. In Self-Similar Network Traffic and Performance Evaluation, chapter 1. Wiley-Interscience, NY, 2000.

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K. Park and W. Willinger. Self-similar network traffic: An Overview. In Self-Similar Network Traffic and Performance Evaluation, chapter 1. Wiley-Interscience, NY, 2000.

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K. Park and W. Willinger, "Self-similar network traffic: an overview ". In Self-Similar Network Traffic and Performance Evaluation, K. Park and W. Willinger (eds.), pp. 1--38, John Wiley & Sons, 2000.

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K. Park and W. Willinger. Self-similar network traffic: An overview. In Self-Similar Network Traffic and Performance Evaluation. WileyInterscience, 2000.

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K. Park and W. Willinger. Self-similar network traffic: An overview. In Self-Similar Network Traffic and Performance Evaluation. WileyInterscience, 2000.

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