Parameter estimation of Markov modulated fluid arrival processes

IF 1 4区计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Performance Evaluation Pub Date : 2022-10-01 DOI:10.1016/j.peva.2022.102316

Salah Al-Deen Almousa , Gábor Horváth , Miklós Telek

引用次数: 1

Abstract

Markov modulated discrete arrival processes have a wide literature, including parameter estimation methods based on expectation–maximization (EM). In this paper, we investigate the adaptation of these EM based methods to Markov modulated fluid arrival processes (MMFAP), and conclude that only the generator matrix of the modulating Markov chain of MMFAPs can be approximated by EM based method. For the rest of the parameters, the fluid rates and the fluid variances, we investigate the efficiency of numerical likelihood maximization.

To reduce the computational complexity of the likelihood computation, we accelerate the numerical inverse Laplace transformation step of the procedure with function fitting.

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马尔可夫调制流体到达过程的参数估计

马尔可夫调制离散到达过程有广泛的文献，包括基于期望最大化(EM)的参数估计方法。在本文中，我们研究了这些基于EM的方法对马尔可夫调制流体到达过程(MMFAP)的适应性，并得出结论，只有MMFAP的调制马尔可夫链的发生器矩阵才能被基于EM的方法近似。对于其他参数，流体速率和流体方差，我们研究了数值似然最大化的效率。为了降低似然计算的计算复杂度，我们利用函数拟合加速了该过程的数值拉普拉斯逆变换步骤。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Performance Evaluation 工程技术-计算机：理论方法

CiteScore

3.10

自引率

0.00%

发文量

审稿时长

24 days

期刊介绍： Performance Evaluation functions as a leading journal in the area of modeling, measurement, and evaluation of performance aspects of computing and communication systems. As such, it aims to present a balanced and complete view of the entire Performance Evaluation profession. Hence, the journal is interested in papers that focus on one or more of the following dimensions: -Define new performance evaluation tools, including measurement and monitoring tools as well as modeling and analytic techniques -Provide new insights into the performance of computing and communication systems -Introduce new application areas where performance evaluation tools can play an important role and creative new uses for performance evaluation tools. More specifically, common application areas of interest include the performance of: -Resource allocation and control methods and algorithms (e.g. routing and flow control in networks, bandwidth allocation, processor scheduling, memory management) -System architecture, design and implementation -Cognitive radio -VANETs -Social networks and media -Energy efficient ICT -Energy harvesting -Data centers -Data centric networks -System reliability -System tuning and capacity planning -Wireless and sensor networks -Autonomic and self-organizing systems -Embedded systems -Network science