Network queue and loss analysis using histogram-based traffic models

Hernández-Orallo, Enrique; Vila-Carbó, J.

doi:10.1016/j.comcom.2009.08.011

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…There is a clear evidence of the importance of the Hurst parameter in network traffic models based on traditional queueing models [8,11,20,26]. This paper shows the importance in real-time analysis too.…”

Section: Schedulability Analysismentioning

confidence: 97%

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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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Section: Schedulability Analysismentioning

confidence: 97%

“…The strong impact of self-similarity on packet scheduling has been clearly shown [8,11,12,20,30]. The main effect is an increase in the packet loss rate and in the packet delays due to greater buffer delays.…”

mentioning

confidence: 92%

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

Hernández-Orallo

Vila-Carbó

2012

Real-Time Syst

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“…Following Hernández-Orallo [3,4,5], we use a discrete time queueing model. The number of transmission units produced by the corresponding traffic source during the k th slot is denoted by discrete random variable A(k).…”

Section: Queueing Modelmentioning

confidence: 99%

“…The first work was proposed by Skelly et al [6] in the area of network calculus to model the video sources and to predict buffer occupancy distributions. More recently, Hernàndez et al [3,4,5] have proposed a performance analysis model to obtain histograms of buffer occupancy. Their approach, called HBSP (Histogram Based Stochastic Process) works directly with histograms using a set of specific operators in discrete time.…”

Section: Introductionmentioning

confidence: 99%

“…Such an approximation on the processes involved in the queueing model may lead to incorrect results (see [2] for service time distributions approximated by a Gaussian distribution). Instead, we construct empirical distributions which are stochastic bounds of the traffic distribution, while Hernàndez et al [3,4,5] only build approximations of this distribution. Thanks to the monotonicity of the model, we use these empirical distributions in the queuing model to obtain upper and lower bounds on the performance indices.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of a Queue by Combining Stochastic Bounds, Real Traffic Traces and Histograms

Aït-Salaht

Castel-Taleb

Fourneau

et al. 2016

The Computer Journal

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We present an approach to derive performance bounds of a queue under histogram-based input traffics. The results are obtained through strong stochastic bounds on the queue length and on the output traffic. The bounds provide probability inequalities on transient behaviors and on steady-state when it exists. We consider both stationary and non stationary traffics and provide some numerical techniques in both cases. Unlike approximate methods, these bounds can be used to check if the Quality of Service constraints are satisfied or not. Our approach provides a trade-off between the accuracy of results and the computational complexity and it is much faster than the histogram-based simulation.

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