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[Defense] Application Agnostic Network Traffic Modeling for Realistic Traffic Generation

Thursday, November 5, 2020

1:00 pm - 3:00 pm

In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
Oluwamayowa Adeleke
will defend his dissertation
Application Agnostic Network Traffic Modeling for Realistic Traffic Generation


Abstract

Research and testing in networking sometimes require experiments that utilize real or representative application network traffic. However, the process for obtaining production network traffic data from industry partners is a significant pain point for many researchers in academia because of restrictive privacy policies. Thus researchers typically resort to synthetic traffic generators. Our survey of over 7000 networking research papers revealed that most research projects exclusively use generators that do not always produce realistic application traffic workloads in their evaluation experiments. Therefore, this research focuses on developing a new application-agnostic framework for producing abstract, high-fidelity models of application network traffic patterns for realistic traffic generation in laboratory environments. The framework includes a comprehensive evaluation system for realistic traffic generation models. We evaluate the methods and algorithms applied in the framework. We created and evaluated a new application traffic modeling method that combines clustering methods with stochastic modeling for realistic traffic modeling. The evaluation results show that traffic generated is similar to actual production traffic for many types of applications. The outcome and methods derived from this work significantly improve how experimentation with realistic workloads on datacenter, cloud, enterprise, and IoT networks.


 Thursday, November 5, 2020
1:00PM - 3:00PM CDT
Online via MS Teams

Dr. Deniz Gurkan, dissertation advisor

Faculty, students and the general public are invited.

Location
Online via MS Teams