An Online Learning Multi-path Selection Framework for Multi-path Transmission Protocols
Refereed conference paper presented and published in conference proceedings


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AbstractIn the last decade, we have witnessed a tremendous growth of inter-connectivity among hosts in networks. Many new data transmission protocols have been developed to enable multi-path data transmissions between two hosts. However, the existing multi-path transmission protocol designs are limited as they neglect the stochastic nature of the metrics of the paths, e.g., latency, available bandwidth, and packet loss. Moreover, there are different design requirements in the applications, such as low latency, bandwidth throttling, and low loss rate in data delivery. In this paper, we propose a flexible online learning multi-path selection (OLMPS) framework to select multiple paths by learning the stochastic metrics of the paths and meeting the design requirements of the applications. Specifically, we design a set of novel online learning algorithms in the OLMPS framework for three different applications, maxRTT constrained, bandwidth constrained, and loss rate constrained, multi-path selection, to select paths and satisfy the requirements. We prove that the algorithms can provide theoretical guarantees on both sublinear regret and sublinear violation in our OLMPS framework.
Acceptance Date15/01/2019
All Author(s) ListKechao Cai, John C.S. Lui
Name of Conference53rd Annual Conference on Information Sciences and Systems (CISS)
Start Date of Conference20/03/2019
End Date of Conference22/03/2019
Place of ConferenceBaltimore
Country/Region of ConferenceUnited States of America
Proceedings Title2019 53rd Annual Conference on Information Sciences and Systems (CISS)
Year2019
Month3
PublisherIEEE
ISBN978-1-7281-1151-3
LanguagesEnglish-United States

Last updated on 2020-28-03 at 02:39