Application Agnostic Network Traffic Modeling for Realistic Traffic Generation

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dc.contributor.advisorGurkan, Deniz
dc.contributor.committeeMemberSubhlok, Jaspal
dc.contributor.committeeMemberGabriel, Edgar
dc.contributor.committeeMemberLent, Ricardo
dc.creatorAdeleke, Oluwamayowa Ade
dc.date.accessioned2021-08-06T19:07:15Z
dc.date.createdDecember 2020
dc.date.issued2020-12
dc.date.submittedDecember 2020
dc.date.updated2021-08-06T19:07:16Z
dc.description.abstractResearch 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.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.citationPortions 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.
dc.identifier.urihttps://hdl.handle.net/10657/8020
dc.language.isoeng
dc.rightsThe 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).
dc.subjectcomputer networks
dc.subjecttraffic modeling
dc.subjecttraffic generators
dc.subjectrealistic traffic generation
dc.titleApplication Agnostic Network Traffic Modeling for Realistic Traffic Generation
dc.type.dcmiText
dc.type.genreThesis
local.embargo.lift2022-12-01
local.embargo.terms2022-12-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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