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Title:FCoder: A real-time large-scale bottleneck detection mechanism with neural network and transfer learning
Author(s):Zhu, Zhoushi
Advisor(s):Hu, Yih-Chun
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Shared bottleneck detection
Machine learning
Transfer learning
Siamese neural network
TCP Multipath
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.
Issue Date:2021-04-27
Type:Thesis
URI:http://hdl.handle.net/2142/110867
Rights Information:Copyright 2021 Zhoushi Zhu
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05


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