Internet Traffic Tends Toward Poisson and Independent as the Load Increases

Cao, Jin; Cleveland, William S.; Lin, Defu; Sun, Danfeng

doi:10.1007/978-0-387-21579-2_6

Cited by 168 publications

(119 citation statements)

References 20 publications

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“…Thus, it appears that the output queueing delay distribution gets closer to an exponential distribution for higher output link utilizations. A similar finding was also reported in [16]. Nevertheless, further analysis is needed to confirm such a statement.…”

Section: Queuing Delay Tail Behaviorsupporting

confidence: 83%

Measurement and analysis of single-hop delay on an IP backbone network

Papagiannaki

Moon

Fraleigh³

et al. 2003

IEEE J. Select. Areas Commun.

150

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Abstract-We measure and analyze the single-hop packet delay through operational routers in the Sprint Internet protocol (IP) backbone network. After presenting our delay measurements through a single router for OC-3 and OC-12 link speeds, we propose a methodology to identify the factors contributing to single-hop delay. In addition to packet processing, transmission, and queueing delay at the output link, we observe the presence of very large delays that cannot be explained within the context of a first-in first-out output queue model. We isolate and analyze these outliers.Results indicate that there is very little queueing taking place in Sprint's backbone. As link speeds increase, transmission delay decreases and the dominant part of single-hop delay is packet processing time. We show that if a packet is received and transmitted on the same linecard, it experiences less than 20 s of delay. If the packet is transmitted across the switch fabric, its delay doubles in magnitude. We observe that processing due to IP options results in single-hop delays in the order of milliseconds. Milliseconds of delay may also be experienced by packets that do not carry IP options. We attribute those delays to router idiosyncratic behavior that affects less than 1% of the packets. Finally, we show that the queueing delay distribution is long-tailed and can be approximated with a Weibull distribution with the scale parameter = 0 5 and the shape parameter = 0 6 to 0.82. Index Terms-Link utilization, queueing delay, single-hop delay measurement.

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Section: Queuing Delay Tail Behaviorsupporting

confidence: 83%

Measurement and analysis of single-hop delay on an IP backbone network

Papagiannaki

Moon

Fraleigh³

et al. 2003

IEEE J. Select. Areas Commun.

150

View full text Add to dashboard Cite

show abstract

“…This study also identified the proportion of dense flows (those generating bursts of densely clustered packet arrivals) vs sparse flows in the traffic aggregate as the determining factor in the amount of correlation present. Studies from Bell Labs ( [4], [5] and [6]) attributed the tendency of packet inter-arrival times to become independent and exponential (Poisson) to increasing numbers of active TCP connections sharing a link. Another recent study of backbone traffic [19] claimed that a stationary Poisson model characterized traffic arrivals up to sub-second time scales…”

Section: Other Related Researchmentioning

confidence: 99%

Long-range dependence in a changing Internet traffic mix

et al. 2005

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This paper provides a deep analysis of long-range dependence in a continually evolving Internet traffic mix by employing a number of recently developed statistical methods. Our study considers time-of-day, day-of-week, and cross-year variations in the traffic on an Internet link. Surprisingly large and consistent differences in the packet-count time series were observed between data from 2002 and 2003. A careful examination, based on stratifying the data according to protocol, revealed that the large difference was driven by a single UDP application that was not present in 2002. Another result was that the observed large differences between the two years showed up only in packet-count time series, and not in byte counts (while conventional wisdom suggests that these should be similar). We also found and analyzed several of the time series that exhibited more "bursty" characteristics than could be modeled as Fractional Gaussian Noise. The paper also shows how modern statistical tools can be used to study long-range dependence and non-stationarity in Internet traffic data.

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“…This assumption is valid if there are many sources sharing the same link, as the traffic generated by a large number of sources tends to Poisson as the load increases due to statistical multiplexing [36]. We believe that this assumption will not influence our results with respect to the effects of the PSD, on the other hand it keeps the number of parameters of the model low, and thus eases readability.…”

Section: Performance Analysismentioning

confidence: 97%

On the effects of the packet size distribution on FEC performance

2006

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For multimedia traffic like VBR video, knowledge of the average loss probability is not sufficient to determine the impact of loss on the perceived visual quality and on the possible ways of improving it, for example by forward error correction (FEC) and error concealment. In this paper we investigate how the packet size distribution affects the packet loss process, i.e. the probability of consecutive losses and the distribution of the number of packets lost in a block of packets and the related FEC performance. We present an exact mathematical model for the loss process of an MMPP + MMPP/E r /1/K queue and compare the results of the model to simulations performed with various other packet size distributions (PSDs), among others, the measured PSD from an Internet backbone. The results show that analytical models of the PSD matching the first three moments (mean,variance and skewness) of the empirical PSD can be used to evaluate the performance of FEC in real networks. We conclude that the exponential PSD, though it is not a worst case scenario, is a good approximation for the PSD of today's Internet to evaluate FEC performance. We also conclude that the packet size distribution affects the packet loss process and thus the efficiency of FEC mainly in access networks where a single multimedia stream might affect the multiplexing behavior. We evaluate how the PSD affects the accuracy of the widely used Gilbert model to calculate FEC performance and conclude that the Gilbert model can capture loss correlations better if the CoV of the PSD is high.

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Internet Traffic Tends Toward Poisson and Independent as the Load Increases

Cited by 168 publications

References 20 publications

Measurement and analysis of single-hop delay on an IP backbone network

Measurement and analysis of single-hop delay on an IP backbone network

Long-range dependence in a changing Internet traffic mix

On the effects of the packet size distribution on FEC performance

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