{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110867"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110867","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"FCoder: A real-time large-scale bottleneck detection mechanism with neural network and transfer learning","abstract":"Detecting shared bottlenecks among network flows is crucial in TCP Multipath to ensure TCP fairness and other applications related to cross-flow congestion control. The problem of whether two flows share a bottleneck has been well investigated in previous work, but a large-scale bottleneck detection scheme among a large number of flows remains not fully explored. In this thesis, we explore how to scale the pairwise shared bottleneck detection mechanism to work with a large number of flows using machine learning techniques. We can use machine learning techniques to preselect possible candidate pairs before pairwise comparison. It improves the time efficiency, reduces waste of computational resources, and enables scaling to detect among a large number of flows, compared with pairwise examining all existing pairs of flows. To validate the idea, we present fCoder, a solution for large-scale shared bottleneck detection for Internet-like networks that detect co-bottlenecks using a correlation mechanism based on time-domain samples of the round-trip-time (RTT) of flows. We accelerate the process using machine learning techniques to enable detection among a large number of flows in real-time. We evaluate the detection performance and time efficiency of fCoder to demonstrate that Machine Learning is a promising technique that helps scaling shared bottleneck detection approaches to detect shared bottlenecks among a large number of flows. In particular, we have shown in our experiment that we can process 100 times more pairs of flows by training a Dense Neural Network (DNN) using simulated network traces data. We have also shown that we can use the transfer learning technique to tune the DNN using a small amount of real-world data to accurately detect real-world datasets.","abstract_html":"Detecting shared bottlenecks among network flows is crucial in TCP Multipath to ensure TCP fairness and other applications related to cross-flow congestion control. The problem of whether two flows share a bottleneck has been well investigated in previous work, but a large-scale bottleneck detection scheme among a large number of flows remains not fully explored. In this thesis, we explore how to scale the pairwise shared bottleneck detection mechanism to work with a large number of flows using machine learning techniques. We can use machine learning techniques to preselect possible candidate pairs before pairwise comparison. It improves the time efficiency, reduces waste of computational resources, and enables scaling to detect among a large number of flows, compared with pairwise examining all existing pairs of flows. To validate the idea, we present fCoder, a solution for large-scale shared bottleneck detection for Internet-like networks that detect co-bottlenecks using a correlation mechanism based on time-domain samples of the round-trip-time (RTT) of flows. We accelerate the process using machine learning techniques to enable detection among a large number of flows in real-time. We evaluate the detection performance and time efficiency of fCoder to demonstrate that Machine Learning is a promising technique that helps scaling shared bottleneck detection approaches to detect shared bottlenecks among a large number of flows. In particular, we have shown in our experiment that we can process 100 times more pairs of flows by training a Dense Neural Network (DNN) using simulated network traces data. We have also shown that we can use the transfer learning technique to tune the DNN using a small amount of real-world data to accurately detect real-world datasets.","abstract_has_math":false,"creators":["Zhu, Zhoushi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Hu, Yih-Chun"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T04:06:55Z","date_published":"2021-09-17T04:06:55Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Shared bottleneck detection","Machine learning","Transfer learning","Siamese neural network","TCP Multipath"],"languages":["en"],"rights":["Copyright 2021 Zhoushi Zhu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110867","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hu, Yih-Chun"]},{"key":"dc:creator","label":"Author","values":["Zhu, Zhoushi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T04:06:55Z","2023-09-17T04:07:01Z","2021-04-27","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Shared bottleneck detection","Machine learning","Transfer learning","Siamese neural network","TCP Multipath"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Zhoushi Zhu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110867"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Detecting shared bottlenecks among network flows is crucial in TCP Multipath to ensure TCP fairness and other applications related to cross-flow congestion control. The problem of whether two flows share a bottleneck has been well investigated in previous work, but a large-scale bottleneck detection scheme among a large number of flows remains not fully explored. In this thesis, we explore how to scale the pairwise shared bottleneck detection mechanism to work with a large number of flows using machine learning techniques. We can use machine learning techniques to preselect possible candidate pairs before pairwise comparison. It improves the time efficiency, reduces waste of computational resources, and enables scaling to detect among a large number of flows, compared with pairwise examining all existing pairs of flows. To validate the idea, we present fCoder, a solution for large-scale shared bottleneck detection for Internet-like networks that detect co-bottlenecks using a correlation mechanism based on time-domain samples of the round-trip-time (RTT) of flows. We accelerate the process using machine learning techniques to enable detection among a large number of flows in real-time. We evaluate the detection performance and time efficiency of fCoder to demonstrate that Machine Learning is a promising technique that helps scaling shared bottleneck detection approaches to detect shared bottlenecks among a large number of flows. In particular, we have shown in our experiment that we can process 100 times more pairs of flows by training a Dense Neural Network (DNN) using simulated network traces data. We have also shown that we can use the transfer learning technique to tune the DNN using a small amount of real-world data to accurately detect real-world datasets.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-05-01","The student, Zhoushi Zhu, accepted the attached license on 2021-04-27 at 12:07.","The student, Zhoushi Zhu, submitted this Thesis for approval on 2021-04-27 at 12:14.","This Thesis was approved for publication on 2021-04-27 at 14:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16584 on 2021-09-16 at 20:14:38","Made available in DSpace on 2021-09-17T04:06:55Z (GMT). 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The problem of whether two flows share a bottleneck has been well investigated in previous work, but a large-scale bottleneck detection scheme among a large number of flows remains not fully explored. In this thesis, we explore how to scale the pairwise shared bottleneck detection mechanism to work with a large number of flows using machine learning techniques. We can use machine learning techniques to preselect possible candidate pairs before pairwise comparison. It improves the time efficiency, reduces waste of computational resources, and enables scaling to detect among a large number of flows, compared with pairwise examining all existing pairs of flows. To validate the idea, we present fCoder, a solution for large-scale shared bottleneck detection for Internet-like networks that detect co-bottlenecks using a correlation mechanism based on time-domain samples of the round-trip-time (RTT) of flows. We accelerate the process using machine learning techniques to enable detection among a large number of flows in real-time. We evaluate the detection performance and time efficiency of fCoder to demonstrate that Machine Learning is a promising technique that helps scaling shared bottleneck detection approaches to detect shared bottlenecks among a large number of flows. In particular, we have shown in our experiment that we can process 100 times more pairs of flows by training a Dense Neural Network (DNN) using simulated network traces data. We have also shown that we can use the transfer learning technique to tune the DNN using a small amount of real-world data to accurately detect real-world datasets.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-05-01","The student, Zhoushi Zhu, accepted the attached license on 2021-04-27 at 12:07.","The student, Zhoushi Zhu, submitted this Thesis for approval on 2021-04-27 at 12:14.","This Thesis was approved for publication on 2021-04-27 at 14:34.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16584 on 2021-09-16 at 20:14:38","Made available in DSpace on 2021-09-17T04:06:55Z (GMT). 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