%0 Conference Paper %1 kha19ITC31 %A Khangura, Sukhpreet Kaur %A Akin, Sami %B 31th International Teletraffic Congress (ITC 31) %C Budapest, Hungary %D 2019 %K itc itc31 %T Measurement-based Online Available Bandwidth Estimation employing Reinforcement Learning %U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc31/kha19ITC31.pdf?inline=true %X An accurate and fast estimation of the available bandwidth in a network with varying cross traffic is a challenging task. The accepted probing tools, based on the fluid-flow model of a bottleneck link with first-in-first-out multiplexing, estimate the available bandwidth by measuring packet dispersions. The estimation becomes more difficult if the packet dispersions deviate from the assumptions of the fluid-flow model in the presence of non-fluid bursty cross traffic, multiple bottleneck links, and inaccurate time-stamping. This motivates us to explore the use of machine learning tools for available bandwidth estimation. Hence, we consider reinforcement learning and implement the single state multi-armed bandit technique, which follows the ε-greedy algorithm to find the available bandwidth. Our measurements and tests reveal that our proposed method identifies the available bandwidth with high precision. Furthermore, our method converges to the available bandwidth under a variety of notoriously difficult conditions such as heavy traffic burstiness, different cross traffic intensities, multiple bottleneck links, and in networks where tight link differs from bottleneck link. Compared to the piecewise linear network model-based direct technique that employs Kalman filter, our method shows accurate and faster convergence in many certain network scenarios and does not require measurement noise statistics.

@inproceedings{kha19ITC31, abstract = {An accurate and fast estimation of the available bandwidth in a network with varying cross traffic is a challenging task. The accepted probing tools, based on the fluid-flow model of a bottleneck link with first-in-first-out multiplexing, estimate the available bandwidth by measuring packet dispersions. The estimation becomes more difficult if the packet dispersions deviate from the assumptions of the fluid-flow model in the presence of non-fluid bursty cross traffic, multiple bottleneck links, and inaccurate time-stamping. This motivates us to explore the use of machine learning tools for available bandwidth estimation. Hence, we consider reinforcement learning and implement the single state multi-armed bandit technique, which follows the ε-greedy algorithm to find the available bandwidth. Our measurements and tests reveal that our proposed method identifies the available bandwidth with high precision. Furthermore, our method converges to the available bandwidth under a variety of notoriously difficult conditions such as heavy traffic burstiness, different cross traffic intensities, multiple bottleneck links, and in networks where tight link differs from bottleneck link. Compared to the piecewise linear network model-based direct technique that employs Kalman filter, our method shows accurate and faster convergence in many certain network scenarios and does not require measurement noise statistics.}, added-at = {2020-04-29T15:29:04.000+0200}, address = {Budapest, Hungary}, author = {Khangura, Sukhpreet Kaur and Akin, Sami}, biburl = {https://www.bibsonomy.org/bibtex/2838b811aff0c0482a118f952e60c056d/itc}, booktitle = {31th International Teletraffic Congress (ITC 31)}, interhash = {3fd2439e3fa1030d34e703b17683fd7b}, intrahash = {838b811aff0c0482a118f952e60c056d}, keywords = {itc itc31}, timestamp = {2020-04-30T18:18:45.000+0200}, title = {Measurement-based Online Available Bandwidth Estimation employing Reinforcement Learning}, url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc31/kha19ITC31.pdf?inline=true}, year = 2019 }