Design and Analysis of Computer Algorithms
Author: Xiaodong Wang | Press: Publishing House of Electronics Industry.View Website
Hello! Welcome to my personal homepage. I am Runqun Xiong
An assistant professor in School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China. I received my B.S. degree in Applied Mathematics from Southeast University in 2005, and received my Ph.D. degree in Computer Science from Southeast University under the supervision of Professor Junzhou Luo in 2015. My current research interests include Parallel and Distributed Computing, Cloud Computing, Industrial Network and Massive Data Storage Management.
I worked at the European Organization for Nuclear Research (CERN) as a research associate for AMS-02 experiment from June 2011 to July 2012. Until now, I am still working for AMS-02 data processing in AMS SOC (Science Operations Center) at Southeast University. I am also involved in various national, provincial and ministry level research projects and have published papers in conferences such as International Conference on Parallel Processing (ICPP), IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), journals such as Physical Review Letters (PRL), Cluster Computing and Journal on Communications.
The Parallel and Distributed Computing field research emphasizes on scalable, fault-tolerant algorithms for distributed systems, especially for the management and analysis of big data. We are also responsible for the planning, procurement, installation, and maintenance of the information and communication technology for AMS-02 SOC at SEU.
Cloud Computing offers many advantages to researchers and engineers who need access to high performance computing facilities for solving particular compute-intensive and/or large-scale problems, especially in the Big Data era, there are a number of fundamental problems which must be addressed.
The fourth industrial revolution has been unveiled with the rapid development of Internet of Things (IoT), Cloud Computing and Big Data. Industrial Network, as a product of in-depth integration and the novel application of Internet related technologies to industry, is the core of this revolution. We propose a breakthrough innovation in the architecture and key technologies of Industrial Network.
To maximize speed, a storage infrastructure must have low-latency capabilities and the ability to rapidly scale up, in and out. The source of analytics must be close to the systems of record. Automatic policy engines and analytics-driven data management keep data in the right place. Data has to move to the fastest storage for analysis and then retreat to lower-cost storage when not in use.
An assistant professor in School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China.
Worked for AMS-02 data processing in AMS SOC (Science Operations Center) at CERN, CH-1211, Geneva 23, Switzerland.
Thesis: Research on Replica Placement and Selection Strategies in Heterogeneous Cluster Storage System for Big Data.
Thesis: Design and Implementation of Online Examination System.
funded by National Science Foundation of China (NSFC), Southeast University of China, PI: Dr. Runqun Xiong.
funded by National Science Foundation of Jiangsu Province, Southeast University of China, PI: Dr. Runqun Xiong.
My research work is also supported by NSFC under Grants No.61320106007, No.61572129, No.61502097, Jiangsu Provincial Key Laboratory of Network and Information Security under Grants No.BM2003201, Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grants No.93K-9, and partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization.