Fitting Model for Self-Similar Traffic - Time Dependent Markovian Process and Second Order Statistics

被引：0

作者：

Vollala, Abhilash ^{[1
]}

Perati, Malla Reddy ^{[1
]}

机构：

[1] Kakatiya Univ, Dept Math, Warangal, Andhra Pradesh, India

来源：

STATISTICS AND APPLICATIONS | 2022年 / 20卷 / 01期

关键词：

Self-similar traffic; Variance-time; Markovain Modulated Poisson process (MMPP); Interrupted Poisson process (IPP); TRANSIENT-BEHAVIOR; QUEUES;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Various self-similar traffic models proposed earlier are asymptotic. In this connection, many Markovian models are proposed, but the performance analysis was possible only when the system is in steady state, and such traffic models are not realistic. In this paper, a procedure is proposed to fit Markov Modulated Poisson Process (MMPP) with time dependent sinusoidal arrival rates that emulates self-similar traffic. This is done by matching the variance of the both during prescribed time scales. Numerical results represent what extent MMPP could reproduce self-similar traffic in specified time scales.

引用

页码：297 / 309

页数：13

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