学术报告
同济大学计算机科学与技术系智信讲坛第(91)期
题目：The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective
报告人：Nei Kato
时间：2017年5月11日周二下午 14:15
地点：电信楼305
组织单位：计算机科学与技术系
邀请人：吴俊教授
报告人简介：
Nei Kato is a full professor and the Director of Research Organization of Electrical Communication(ROEC), Tohoku University, Japan. He has been engaged in research on computer networking, wireless mobile communications, satellite communications, ad hoc & sensor & mesh networks, smart grid, IoT, Big Data, and pattern recognition. He has published more than 350 papers in prestigious peer-reviewed journals and conferences. He is the Editor-in-Chief of IEEE Network Magazine(2015.7-), the Associate Editor-in-Chief of IEEE Internet of Things Journal(2013-), an Area Editor of IEEE Transactions on Vehicular Technology(2014-), and the Chair of IEEE Communications Society Sendai Chapter. He served as a Member-at-Large on the Board of Governors, IEEE Communications Society(2014-2016), a Vice Chair of Fellow Committee of IEEE Computer Society(2016),a member of IEEE Computer Society Award Committee(2015-2016) and IEEE Communications Society Award Committee(2015-2017). He has also served as the Chair of Satellite and Space Communications Technical Committee(2010-2012) and Ad Hoc & Sensor Networks Technical Committee(2014-2015) of IEEE Communications Society. Nei Kato is a Distinguished Lecturer of IEEE Communications Society and Vehicular Technology Society. He is a fellow of IEEE and IEICE.
内容提要：
Recently, deep learning, an emerging machine learning technique, is garnering a lot of research attention in several computer science areas. However, to the best of our knowledge, its application to improve heterogeneous network traffic control which is an important and challenging area by its own merit has yet to appear because of the difficult challenge in characterizing the appropriate input and output patterns for a deep learning system to correctly reflect the highly dynamic nature of large-scale heterogeneous networks. In this talk, an appropriate input and output characterizations of heterogeneous network traffic will be introduced and a supervised deep neural network system will be proposed. I will describe how our proposed system works and how it differs from traditional neural networks. Also, preliminary results will be discussed and I will demonstrate the encouraging performance of our proposed deep learning system compared to a benchmark routing strategy (Open Shortest Path First (OSPF)) in terms of significantly better signaling overhead, throughput, and delay.
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