The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Il a été signalé que le trafic de paquets IP présente une nature auto-similaire et entraîne une dégradation des performances du réseau. Il est donc crucial, pour une conception de tampon appropriée des routeurs et des commutateurs, de prédire le comportement de mise en file d'attente avec une entrée auto-similaire. Il est bien connu que les méthodes d'ajustement basées sur les statistiques de second ordre des décomptes pour le processus d'arrivée ne sont pas suffisantes pour prédire les performances du système de file d'attente avec des entrées auto-similaires. Cependant, des études récentes ont révélé que la probabilité de perte d'un système de file d'attente fini peut être bien approchée par les modèles d'entrée markoviens. Cet article étudie l'impact à l'échelle du temps sur la probabilité de perte de PPMM/D/ 1 /K système où le PPMM est généré de manière à correspondre à la variance du processus auto-similaire sur des échelles de temps spécifiées. Nous étudions la probabilité de perte en termes de taille du système, de paramètres de Hurst et d'échelles de temps. Nous comparons également la probabilité de perte résultant PPMM/D/ 1 /K avec simulation. Les résultats numériques montrent que la probabilité de perte de PPMM/D/ 1 /K ne sont pas significativement affectés par l’échelle de temps et que la probabilité de perte est bien estimée avec les résultats PPMM/D/ 1 /K.
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Shoji KASAHARA, "Internet Traffic Modeling: Markovian Approach to Self-Similar Traffic and Prediction of Loss Probability for Finite Queues" in IEICE TRANSACTIONS on Communications,
vol. E84-B, no. 8, pp. 2134-2141, August 2001, doi: .
Abstract: It has been reported that IP packet traffic exhibits the self-similar nature and causes the degradation of network performance. Therefore it is crucial for the appropriate buffer design of routers and switches to predict the queueing behavior with self-similar input. It is well known that the fitting methods based on the second-order statistics of counts for the arrival process are not sufficient for predicting the performance of the queueing system with self-similar input. However recent studies have revealed that the loss probability of finite queuing system can be well approximated by the Markovian input models. This paper studies the time-scale impact on the loss probability of MMPP/D/1/K system where the MMPP is generated so as to match the variance of the self-similar process over specified time-scales. We investigate the loss probability in terms of system size, Hurst parameters and time-scales. We also compare the loss probability of resulting MMPP/D/1/K with simulation. Numerical results show that the loss probability of MMPP/D/1/K are not significantly affected by time-scale and that the loss probability is well approximated with resulting MMPP/D/1/K.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e84-b_8_2134/_p
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@ARTICLE{e84-b_8_2134,
author={Shoji KASAHARA, },
journal={IEICE TRANSACTIONS on Communications},
title={Internet Traffic Modeling: Markovian Approach to Self-Similar Traffic and Prediction of Loss Probability for Finite Queues},
year={2001},
volume={E84-B},
number={8},
pages={2134-2141},
abstract={It has been reported that IP packet traffic exhibits the self-similar nature and causes the degradation of network performance. Therefore it is crucial for the appropriate buffer design of routers and switches to predict the queueing behavior with self-similar input. It is well known that the fitting methods based on the second-order statistics of counts for the arrival process are not sufficient for predicting the performance of the queueing system with self-similar input. However recent studies have revealed that the loss probability of finite queuing system can be well approximated by the Markovian input models. This paper studies the time-scale impact on the loss probability of MMPP/D/1/K system where the MMPP is generated so as to match the variance of the self-similar process over specified time-scales. We investigate the loss probability in terms of system size, Hurst parameters and time-scales. We also compare the loss probability of resulting MMPP/D/1/K with simulation. Numerical results show that the loss probability of MMPP/D/1/K are not significantly affected by time-scale and that the loss probability is well approximated with resulting MMPP/D/1/K.},
keywords={},
doi={},
ISSN={},
month={August},}
Copier
TY - JOUR
TI - Internet Traffic Modeling: Markovian Approach to Self-Similar Traffic and Prediction of Loss Probability for Finite Queues
T2 - IEICE TRANSACTIONS on Communications
SP - 2134
EP - 2141
AU - Shoji KASAHARA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E84-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2001
AB - It has been reported that IP packet traffic exhibits the self-similar nature and causes the degradation of network performance. Therefore it is crucial for the appropriate buffer design of routers and switches to predict the queueing behavior with self-similar input. It is well known that the fitting methods based on the second-order statistics of counts for the arrival process are not sufficient for predicting the performance of the queueing system with self-similar input. However recent studies have revealed that the loss probability of finite queuing system can be well approximated by the Markovian input models. This paper studies the time-scale impact on the loss probability of MMPP/D/1/K system where the MMPP is generated so as to match the variance of the self-similar process over specified time-scales. We investigate the loss probability in terms of system size, Hurst parameters and time-scales. We also compare the loss probability of resulting MMPP/D/1/K with simulation. Numerical results show that the loss probability of MMPP/D/1/K are not significantly affected by time-scale and that the loss probability is well approximated with resulting MMPP/D/1/K.
ER -