UC Berkeley

Accurate network modeling is critical to the design of network protocols. Traditional modeling approaches, such as Discrete Time Markov Chains (DTMC) are limited in their ability to model time-varying characteristics. This problem is exacerbated in the wireless domain, where fading events create extreme burstiness of delays, losses, and errors on wireless links. In this paper, we describe the data preconditioning modeling technique that is capable of capturing the statistical characteristics of wired and wireless network traces. We revise our previous developed data preconditioning modeling algorithm, the Markov-based Trace Analysis (MTA), and present the Multiple states MTA (MMTA) algorithm. Our main contributions are methodologies created to quantify the accuracy of network models, methodology to choose the most accurate model for a given network and characteristic of interest (e.g., delay, loss, or error process), and the validation of our data preconditioning modeling algorithms. (C) 2006 Elsevier Inc. All rights reserved.