Statistical properties of MPEG video traffic and their impact on traffic modeling in ATM systems

Rose, Oliver

doi:10.1109/lcn.1995.527368

Cited by 390 publications

(192 citation statements)

References 2 publications

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“…MPEG trace data from several different movie clips [26] were used as input to the simulator. The program takes the burst volume and the burst length of the traffic shaper as its input.…”

Section: Simulationmentioning

confidence: 99%

Traffic shaping for MPEG video transmission over the next generation internet

Alam

Atiquzzaman

Karim

2000

Computer Communications

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The Internet Engineering Task Force (IETF) has proposed the Guaranteed Service (GS) in the Integrated Services model with firm delay and bandwidth guarantees. In this paper, we study the effects of a token bucket traffic shaper at the source on the transmission characteristics of Motion Picture Experts Group (MPEG) compressed video streams over the GS. We develop an analytical model of the traffic shaper, and also carry out numerical simulation of the transmission performance using MPEG trace data from several different video sequences. The analytical model and the numerical simulation results are in excellent agreement. Our study provides a technique to determine the token bucket parameters that have to be specified while setting up a GS flow. Our study reveals that the traffic shaping process gives an end application a wide range of flexibility in controlling the delay. By choosing appropriate token bucket traffic shaper parameters, delay can be reduced significantly without the need to reserve costly network resources for less delay-sensitive applications, while time-critical applications may specify the required token bucket parameters for minimum delay. ᭧

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“…MPEG trace data from several different movie clips [26] were used as input to the simulator. The program takes the burst volume and the burst length of the traffic shaper as its input.…”

Section: Simulationmentioning

confidence: 99%

Traffic shaping for MPEG video transmission over the next generation internet

Alam

Atiquzzaman

Karim

2000

Computer Communications

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“…The Pareto and Weibull distributions are often used to characterize heavy-tailed distributions. Some studies show that multimedia streams exhibit self-similar properties with Hurst values between 0.75 and 0.95 [14,31]. The frame sizes of VBR video (MPEG) are best matched with a heavy-tailed Pareto distribution [14].…”

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confidence: 99%

On the nature and impact of self-similarity in real-time systems

Hernández-Orallo

Vila-Carbó

2012

Real-Time Syst

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On the nature and impact of self-similarity in real-time systems Enrique Hernández-Orallo and Joan Vila-Carbó the date of receipt and acceptance should be inserted later Abstract In real-time systems with highly variable task execution times simplistic task models are insufficient to accurately model and to analyze the system. Variability can be tackled using distributions rather than a single value, but the proper characterization depends on the degree of variability. Self-similarity is one of the deepest kinds of variability. It characterizes the fact that a workload is not only highly variable, but it is also bursty on many time-scales. This paper identifies in which situations this source of indeterminism can appear in a real-time system: the combination of variability in task inter-arrival times and execution times. Although self-similarity is not a claim for all systems with variable execution times, it is not unusual in some applications with real-time requirements, like video processing, networking and gaming.The paper shows how to properly model and to analyze self-similar task sets and how improper modeling can mask deadline misses. The paper derives an analytical expression for the dependence of the deadline miss ratio on the degree of self-similarity and proofs its negative impact on real-time systems performance through system's modeling and simulation. This study about the nature and impact of self-similarity on soft real-time systems can help to reduce its effects, to choose the proper scheduling policies, and to avoid its causes at system design time.

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“…For the multifractal estimator, we choose the order of the moments q = 3. Also shown in Table I are estimates of H obtained from the periodogram-based estimator [15] and R/S plots [3], [10].…”

Section: Hurst Parameter Estimationmentioning

confidence: 99%