Yan Zhang, University of Oslo, Norway
Title: Mobile Edge Computing for Internet of Things
Abstract:
In this talk, we will first present the key concepts and architectures related to mobile edge computing in the era of Internet of Things. Then, we mainly focus on edge computing for 5G, Internet of Vehicles and IoT in general. In such contexts, we will present our recent studies and experiments related to different computation offloading solutions and resource management schemes.
Bio:
Prof. Yan Zhang is Full Professor at the Department of Informatics, University of Oslo, Norway. He received a PhD degree in School of Electrical & Electronics Engineering, Nanyang Technological University, Singapore.
He serves as an Associate Technical Editor of IEEE Communications Magazine, an Editor of IEEE Transactions on Green Communications and Networking, an Editor of IEEE Communications Surveys & Tutorials, an Editor of IEEE Internet of Things Journal, an Editor of Vehicular Technology Magazine, and an Associate Editor of IEEE Access. He serves as chair positions in a number of conferences, including IEEE GLOBECOM 2017, IEEE PIMRC 2016, and IEEE SmartGridComm 2015. He is IEEE VTS (Vehicular Technology Society) Distinguished Lecturer. He serves as IEEE TCGCC Vice Chair. His current research interests include: next-generation wireless networks leading to 5G, green and secure cyber-physical systems (e.g., smart grid, transport, and healthcare).
Guo Song, Full Professor at Department of Computing, The Hong Kong Polytechnic University
Title: Data Driven Resource Management in Collaborative Edge Computing
Abstract:
When accessing cloud-hosted modern applications, users often suffer a significant latency due to the long geo-distance to the central cloud. Edge computing thus emerges as an alternative paradigm that can reduce this latency by deploying services close to users. As data are usually generated on geo-distributed edges, services require the collaboration among them. Allocation of various resources, such as computation units, data and bandwidth between edges, is becoming important. In this talk, we will present our recent studies on data driven resource management among collaborative edges. We will start with our works on cross-cloud resource management, and then propose the new approach on using spatial-temporal request patterns for big data analytics in geo-distributed edges. Some preliminary research results on data-driven data-task joint scheduling will be discussed as well.
Bio:
Song Guo is a Full Professor at Department of Computing, The Hong Kong Polytechnic University. He received his Ph.D. in computer science from University of Ottawa and was a full professor with the University of Aizu, Japan. His research interests are mainly in the areas of big data, cloud computing, green communication and computing, and distributed systems. He has published over 350 conference and journal papers in these areas. His work was included in 21st Annual Best of Computing – Notable Books and Articles in Computing of 2016 by ACM Computing Reviews. He also received 5 best paper awards from IEEE/ACM conferences and the IEEE Systems Journal Annual Best Paper Award of 2017. Dr. Guo currently serves in editorial boards of several prestigious journals, including IEEE Transactions on Emerging Topics in Computing, IEEE Transactions on Sustainable Computing, IEEE Transactions on Green Communications and Networking, and IEEE Communications. He is an active volunteer as General/TPC Chair for 20+ international conferences, Chair/Vice-Chair for several IEEE Technical Committees and SIGs, and keynote speaker and panelist for many domestic and international conferences. He is a senior member of IEEE, a senior member of ACM, and an IEEE Communications Society Distinguished Lecturer.