Realizing IoT & 5G Innovation and Services with SDN/NFV
Jason Yi-Bing Lin
Department of Computer Science
University System of Taiwan
Innovative IoT services such as Tactile Internet, Autonomous Driving and Massive M2M are considered major service offers in the future. These IoT services are characterized by very diverse QoS, security, latency and mobility requirements. Consequently, a more flexible, adaptable network than the existing 4G is required to accommodate and inspire IoT service innovation. For each service innovation, a specific network with the designated QoS, security, latency and mobility features must be provided. Network slicing envisaged by 5G networks will be the answer. SDN/NFV are the key technologies to realize network slicing in 5G. This talk will elaborates on how to devlop SDN/NFV for 5G IoT network slicing.
Yi-Bing Lin received his Bachelor’s degree from National Cheng Kung University, Taiwan, in 1983, and his Ph.D. from the University of Washington, USA, in 1990. From 1990 to 1995 he was a Research Scientist with Bellcore (Telcordia). He then joined the National Chiao Tung University (NCTU) in Taiwan, where he remains. In 2011, Lin became the Vice President of NCTU. Since 2014, Lin has been appointed as the Deputy Minister of Ministry of Science and Technology, Taiwan. After 2016, he become a lifetime Chair Professor of NCTU.Lin is also an Adjunct Research Fellow, Institute of Information Science, Academia Sinica, Research Center for Information Technology Innovation, Academia Sinica, and a member of board of directors, Chunghwa Telecom. He serves on the editorial boards of IEEE Trans. on Vehicular Technology. He is General or Program Chair for prestigious conferences including ACM MobiCom 2002. He is Guest Editor for several journals including IEEE Transactions on Computers. Lin is the author of the books Wireless and Mobile Network
Architecture (Wiley, 2001), Wireless and Mobile All-IP Networks (John
Wiley,2005), and Charging for Mobile All-IP Telecommunications (Wiley,
2008). Lin received numerous research awards including 2005 NSC
Distinguished Researcher, 2006 Academic Award of Ministry of Education and
2008 Award for Outstanding contributions in Science and Technology,
Executive Yuen, 2011 National Chair Award, and TWAS Prize in Engineering
Sciences, 2011 (The World Academy of Sciences). He is in the advisory boards
or the review boards of various government organizations including Ministry
of Economic Affairs, Ministry of Education, and Ministry of Transportation
and Communications. He is Chair of IEEE Taipei Section. Lin is AAAS Fellow,
ACM Fellow, IEEE Fellow, and IET Fellow.
Shou-de Lin is currently a full professor in the CSIE department of National Taiwan University. He holds a BS degree in EE department from National Taiwan University, an MS-EE degree from the University of Michigan, an MS degree in Computational Linguistics and PhD in Computer Science both from the University of Southern California. He leads the Machine Discovery and Social Network Mining Lab in NTU. Before joining NTU, he was a post-doctoral research fellow at the Los Alamos National Lab. Prof. Lin's research includes the areas of machine learning and data mining, social network analysis, and natural language processing. His international recognition includes the best paper award in IEEE Web Intelligent conference 2003, Google Research Award in 2007, Microsoft research award in 2008, 2015, 2016, merit paper award in TAAI 2010, 2014, 2016, best paper award in ASONAM 2011, US Aerospace AFOSR/AOARD research award winner for 5 years. He is the all-time winners in ACM KDD Cup, leading or co-leading the NTU team to win 5 championships. He also leads a team to win WSDM Cup in 2016. He also won the 傑出人才基金會年輕學者創新獎and 吳大猷先生紀念獎. He has served as the senior PC for SIGKDD and area chair for ACL. He is currently the associate editor for International Journal on Social Network Mining, Journal of Information Science and Engineering, and International Journal of Computational Linguistics and Chinese Language Processing. He co-organized three major international data mining competitions including PAKDD Cup 2014, KDD Cup 2016, and WSDM Cup 2018. He is also a freelance writer for Scientific American.
Sampling Large Networks: Algorithms and Applications
John C.S. Lui
Department of Computer Science & Engineering
The Chinese University of Hong Kong,
Shatin, N.T, Hong Kong
Often times, large networks can be represented as graphs. For example, the Internet topology can be represented as an undirected graph while large logical networks (e.g., Facebook, Twitter,..etc) can be represented as either directed or undirected graphs. For these graphs, characterizing node pair relationships is important for applications such as friend recommendation and interest targeting in online social networks (OSNs). Due to the large scale nature of such networks, it is infeasible to enumerate all user pairs and so sampling is used. In this talk, we show that it is a great challenge even for OSN service providers to characterize user pair relationships when they possess the complete graph topology. The reason is that when sampling techniques (i.e., uniform vertex sampling and random walk) are naively applied, they can introduce large biases, in particular, for estimating similarity distribution of user pairs with constraints such as existence of mutual neighbors. Estimating statistics of user pairs is even more challenging in the absence of the complete topology information, since an unbiased sampling technique such as uniform vertex sampling is usually not allowed, and exploring the OSN graph topology is expensive. To address these challenges, we present asymptotically unbiased sampling methods to characterize user pair properties. We also show potential applications and discuss future research work.
John C.S. Lui is currently the Choh-Ming Li Chair Professor in the Department of Computer Science & Engineering (CSE) at The Chinese University of Hong Kong (CUHK). He received his Ph.D. in Computer Science from UCLA. His current research interests are in network sciences which have large data implications, machine learning on large data analytics, network/system security, network economics, cloud computing, large scale distributed systems and performance evaluation theory. John is an active consultant to industry, believing that it is an effective way to do technology transfer and a wonderful way to learn about real and relevant research problems. John is currrently the senior editor in the IEEE/ACM Transactions on Networking, and has been serving in the editorial board of ACM Transactions on Modeling and Performance Evaluation of Computing Systems , IEEE Transactions on Network Science & Engineering, IEEE Transactions on Mobile Computing, IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems, Journal of Performance Evaluation. John served as the chairman of the CSE Department from 2005-2011. He received various departmental teaching awards and the CUHK Vice-Chancellor's Exemplary Teaching Award. John also received the CUHK Faculty of Engineering Research Excellence Award (2011-2012). John is a co-recipient of the best paper award in the IFIP WG 7.3 Performance 2005, IEEE/IFIP NOMS 2006, SIMPLEX 2013, and ACM RecSys 2017. 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 was the past chair of the ACM SIGMETRICS (2011-2015). His personal interests include films and general reading.