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【学术报告】香港中文大学计算机科学与工程系教授、IEEE/ACM Fellow John C.S. Lui学术报告
2019-04-25 11:15   计算机学院 审核人:   (点击: )

报告地点:宝马娱乐app下载长安校区计算机学院105会议室

报告时间:2019年5月6日上午10:30-11:30

报告人:香港中文大学John C.S. Lui教授, IEEE/ACM Fellow

邀请人:褚伟波副教授


报告题目:一类基于在线学习的网络多路径选择方法

报告人简介:

吕自成教授目前是香港中文大学计算机科学与工程系的李卓敏荣誉教授。他的研究方向聚焦于机器学习在网络科学、网络经济学、网络/系统安全、大规模分布式系统和性能测评理论方面的研究与应用。吕教授获得了诸多教学与科研方面的奖项,包括香港中文大学校长模范教学奖和香港中文大学职员杰出研究奖(2011-2012)。他获得了IFIP WG 7.3 Performance 2005, IEEE/IFIP NOMS 2006,SIMPLEX'14,ACM RecSys’17等重要国际会议的最佳学术论文奖以及ACM Mobihoc’18和ASONAM’17会议的最佳论文提名奖。他是IFIP WG 7.3,ACM,IEEE等多个重要协会的会士,Croucher基金会的高级研究专家,以及现任的ACM SIGMETRICS会议主席。吕教授的个人兴趣包括电影和阅读。

报告摘要:

过去十年间,接入计算机网络的主机数量呈现出爆炸式增长。多种用于主机间数据传输的多路径协议被相继提出。然而,当前的数据传输多路径协议由于忽略了网络传输延迟、可用带宽和数据丢包等随机特性而使它们在应用上收到了极大的限制。另外,许多应用对网络传输延迟、链路带宽和丢包率等有一定要求。本报告将介绍一种基于网络特性在线学习的多路径选取框架,用于满足不同应用对数据传输的需求。具体地,将介绍一类用于分别满足最大传输延迟、网络带宽和丢包率约束的在线学习多路径选取算法,并在理论上确保算法具有次线性的regret和violation两个关键性能指标。


Biography:

John C.S. Lui is currently the Choh-Ming Li Professor of the Computer Science & Engineering Department at The Chinese University of Hong Kong. His current research interests are in machine learning on network sciences, network economics, network/system security (e.g., cloud security, mobile security, ...etc), large scale distributed systems and performance evaluation theory. John received various departmental teaching awards and the CUHK Vice-Chancellor's Exemplary Teaching Award, as well as the CUHK Faculty of Engineering Research Excellence Award (2011-2012). He is a co-recipient of the IFIP WG 7.3 Performance 2005, IEEE/IFIP NOMS 2006 and SIMPLEX'14 Best Paper Awards, ACM RecSys’17 best paper award and best paper runner-up in ACM Mobihoc’18 and ASONAM’17. He is an elected member of the IFIP WG 7.3, Fellow of ACM, Fellow of IEEE, Senior Research Fellow of the Croucher Foundation and is currently the chair of the ACM SIGMETRICS. His personal interests include films and general reading.

Title:

An Online Learning Multi-path Selection Framework for Multi-path Transmission Protocols

Abstract:

In 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 talk, 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.

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