On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
A trace-based approach for modeling wireless channel behavior
WSC '96 Proceedings of the 28th conference on Winter simulation
Optimizing the end-to-end performance of reliable flows over wireless links
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
A Markov-based channel model algorithm for wireless networks
MSWIM '01 Proceedings of the 4th ACM international workshop on Modeling, analysis and simulation of wireless and mobile systems
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
Packet Loss Correlation in the MBone Multicast Networ Experimental Measurements and Markov Chain Models
Buffer Coding for Reliable Transmissions over Wireless Networks
Computer Communications
M&M: multi-level Markov model for wireless link simulations
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
An investigation on flexible communications in publish/subscribe services
SEUS'10 Proceedings of the 8th IFIP WG 10.2 international conference on Software technologies for embedded and ubiquitous systems
Improving wireless link simulation using multilevel markov models
ACM Transactions on Sensor Networks (TOSN)
Reliable and Timely Event Notification for Publish/Subscribe Services Over the Internet
IEEE/ACM Transactions on Networking (TON)
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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.