Objectives
Speaker: Shawn Zengxiang Li, Distributed Computing Group, A*Star Institute of High Performance Computing, Singapore
Abstract: In the Big Data era, graph processing has been widely used to represent complex system structure, capture data dependency and uncover relationship insights. Due to the ever-growing graph scale and algorithm complexity, several distributed graph processing frameworks have attracted many interests from both academia and industry. In this talk, I will investigate how to achieve the trade-off between performance and cost for large scale graph processing on the Cloud. System-aware and machine learning models are developed to predict the performance of distributed graph processing tasks. Consequently, cost-efficient resource provisioning strategies could be recommended by selecting a certain number of VMs with specified capability subject to the predefined resource price and user preference. At the end of this talk, I will briefly introduce our recent projects on urban computing, disease simulation and social network analytics based on graph processing and real world data.
Short Bio: Dr. Shawn Zengxiang Li is a scientist in Institute of High Performance Computing, A*Star, Singapore. His research interests include big-data analytics, large-scale graph processing, simulation & modelling, parallel & distributed computing, data center and Cloud computing. He is Principle Investigator and key member of several successful research and industry projects which are relevant to resource management of multi-tenant data center, distributed data/graph processing frameworks, urban computing, transportation optimization, disease simulations, etc. He has published dozens of high quality papers on ACM/IEEE Transaction Journals and Conferences. Dr. Li served as program committee member of several International conferences, including PADS, DS-RT, CloudCom and many others.