Gurkan, Deniz2021-08-06December 22020-12December 2Portions of this document appear in: Adeleke, Oluwamayowa Ade, Nicholas Bastin, and Deniz Gurkan. "Network Testing Using a Novel Framework for Traffic Modeling and Generation." In 2020 29th International Conference on Computer Communications and Networks (ICCCN), pp. 1-2. IEEE, 2020. And in: Adeleke, Oluwamayowa Ade. "Echo-state networks for network traffic prediction." In 2019 IEEE 10th annual information technology, electronics and mobile communication conference (IEMCON), pp. 0202-0206. IEEE, 2019.https://hdl.handle.net/10657/8020Research and testing in networking sometimes require experiments that utilize real application network traffic. However, the process for obtaining production network traffic data from industry partners for testing novel algorithms, protocols, and network functions is a significant pain point for many researchers in academia. Many industry operators are reluctant to share network traffic data with third parties to avoid violating privacy policies and avoid unintentional exposure of proprietary information to competitors. Therefore, many researchers resort to the use of synthetic traffic generators in networking experiments. Our survey of over 7000 networking research papers revealed that most research projects exclusively use constant/maximum throughput traffic generators in their evaluation experiments. These generators do not always generate traffic that is similar to real production traffic. They often blast out packets at fixed rates or rates based on statistical distributions. Existing realistic traffic generators are rarely used, and there is no standardized evaluation system for realistic traffic generators. Therefore, this work focuses on developing a new application-agnostic framework for producing abstract, high-fidelity models of application network traffic patterns for realistic application traffic generation in laboratory environments. The framework includes a comprehensive evaluation system for realistic traffic generation models. We evaluated the methods and algorithms applied in the framework, then we created and evaluated a new application traffic modeling method that combines clustering methods with stochastic modeling for realistic traffic modeling. The evaluation results reveal that traffic generated is similar to actual production traffic for many types of applications. This work's outcome is vital to researchers and industry operators in computer networking, especially those involved in large scale enterprise, data-center, and internet of things (IoT) network testing. The methods presented make it easy to investigate how various changes in a network's traffic patterns and infrastructure can impact its performance. Researchers can test new protocols and algorithms with realistic traffic derived from actual applications, without violating privacy policies or replaying extra-large traffic trace files.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).computer networkstraffic modelingtraffic generatorsrealistic traffic generation2021-08-06Thesisborn digital