Performance impacts of multi-scaling in wide area TCP/IP traffic

Erramilli, Ashok; Narayan, Onuttom; Neidhardt, Arnold L.; Saniee, Iraj

doi:10.1109/infcom.2000.832205

Cited by 83 publications

(73 citation statements)

References 11 publications

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“…We use the tool to formally check if the queueing delay distribution is heavy tailed [15]. The obtained results indicate 5 The probability density function of a Weibull distribution is given by f(x) = that our delay distributions do not have the power-law tail like the Pareto distribution, and are not heavy tailed. We then look into whether our queueing delay distributions are long-tailed.…”

Section: Queuing Delay Tail Behaviormentioning

confidence: 99%

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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 Behaviormentioning

confidence: 99%

“…Follow-up work shows that the wide-area network traffic is multifractal and exhibits varying scaling behavior depending on the time scale [4]. Recent work reveals that the queueing behavior can be approximated differently depending on the link utilization [5].…”

mentioning

confidence: 99%

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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“…More recently, a multifractal behavior, which is typically associated with networking mechanisms operating on small time scales, was discovered in several traces of Internet WAN traffic [20][21][22][23][24]. In general, these characteristics include the long-range dependence (LRD) property and can have a significant impact on the network performance.…”

Section: Introductionmentioning

confidence: 99%

Modeling IP traffic: joint characterization of packet arrivals and packet sizes using BMAPs

2004

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This paper proposes a traffic model and a parameter fitting procedure that are capable of achieving accurate prediction of the queuing behavior for IP traffic exhibiting long-range dependence. The modeling process is a discrete-time batch Markovian arrival process (dBMAP) that jointly characterizes the packet arrival process and the packet size distribution. In the proposed dBMAP, packet arrivals occur according to a discrete-time Markov modulated Poisson process (dMMPP) and each arrival is characterized by a packet size with a general distribution that may depend on the phase of the dMMPP. The fitting procedure is designed to provide a close match of both the autocovariance and the marginal distribution of the packet arrival process, using a dMMPP; a packet size distribution is fitted individually to each state of the dMMPP. A major feature of the procedure is that the number of states of the fitted dBMAP is not fixed a priori; it is determined as part of the procedure itself. In this way, the procedure allows establishing a compromise between the accuracy of the fitting and the number of parameters, while maintaining a low computational complexity.We apply the inference procedure to several traffic traces exhibiting long-range dependence. Very good results were obtained since the fitted dBMAPs match closely the autocovariance, the marginal distribution and the queuing behavior of the measured traces. Our results also show that ignoring the packet size distribution and its correlation with the packet arrival process can lead to large errors in terms of queuing behavior.

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“…Nevertheless, the lower bound set by the ideal predictor is due to the distribution of packet arrivals within the prediction interval. In short timescales, Internet traffic is known to have multifractal scaling [2] and possibly a non-Gaussian marginal distribution with high variability [12]. Such variability affects queueing performance even if we can determine the number of bytes in the prediction interval with no error.…”

Section: B Burst Switchingmentioning

confidence: 99%

“…Internet traffic presents long range dependence, nonstationarity and multifractal scaling in short timescales [1], [2]. While a-priori link dimensioning is difficult to achieve in practice, the traffic correlation can be exploited to provide dynamic bandwidth allocation.…”

Section: Introductionmentioning

confidence: 99%

On linear prediction of Internet traffic for packet and burst switching networks

Morató

Aracil²,

Díez³

et al.

Proceedings Tenth International Conference on Computer Communications and Networks (Cat. No.01EX495)

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Abstract-In this paper, we show that prediction algorithms in the least mean square error sense prove better in a burst rather than in a packet switching network. For the latter, further information about the packet arrival distribution within the prediction interval is required. Regarding burst switching, we compare Optical Burst Switching networks with and without linear prediction to conclude that linear prediction provides a significant improvement in end-to-end latency with low bandwidth waste.

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Performance impacts of multi-scaling in wide area TCP/IP traffic

Cited by 83 publications

References 11 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

Modeling IP traffic: joint characterization of packet arrivals and packet sizes using BMAPs

On linear prediction of Internet traffic for packet and burst switching networks

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