{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/8020"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/8020","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Application Agnostic Network Traffic Modeling for Realistic Traffic Generation","abstract":"Research 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.","abstract_html":"Research 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.","abstract_has_math":false,"creators":["Adeleke, Oluwamayowa Ade"],"institution":"University of Houston","degree_name":"Doctor of Philosophy","degree_level":"Doctoral","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Gurkan, Deniz"],"committee_chairs":[],"committee_members":["Subhlok, Jaspal","Gabriel, Edgar","Lent, Ricardo"],"year":2020,"date_issued":"2020-12","date_published":"2020-12","updated_at":"2026-07-24T02:32:54Z","subjects":["computer networks","traffic modeling","traffic generators","realistic traffic generation"],"languages":["eng"],"rights":["The 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)."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10657/8020","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gurkan, Deniz"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Subhlok, Jaspal","Gabriel, Edgar","Lent, Ricardo"]},{"key":"dc:creator","label":"Author","values":["Adeleke, Oluwamayowa Ade"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-08-06T19:07:15Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-12"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer networks","traffic modeling","traffic generators","realistic traffic generation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["The author of this work is the copyright owner. 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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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Application Agnostic Network Traffic Modeling for Realistic Traffic Generation"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gurkan, Deniz"],"dc:contributor.committeemember":["Subhlok, Jaspal","Gabriel, Edgar","Lent, Ricardo"],"dc:creator":["Adeleke, Oluwamayowa Ade"],"dc:date.accessioned":["2021-08-06T19:07:15Z"],"dc:date.issued":["2020-12"],"dc:description.abstract":["Research 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:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/10657/8020"],"dc:language.iso":["eng"],"dc:rights":["The 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:subject":["computer networks","traffic modeling","traffic generators","realistic traffic generation"],"dc:title":["Application Agnostic Network Traffic Modeling for Realistic Traffic Generation"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:54Z"}