Next-generation applications in science, industry, and business domains are producing colossal amounts of data, now frequently termed as “big data”, which must be analyzed in a timely manner for knowledge discovery and technological innovation. Among many practical computing solutions, workflows have been increasingly employed as an important technique for big data analytics in physical and virtual environments, which has brought many new challenges, such as reducing financial cost in clouds and saving energy consumption in data centers in addition to meeting traditional performance optimization goals. In this talk, I will present my work on performance optimization and energy efficiency for big-data workflow scheduling in high-performance computing.
 
Speaker Biography:
Tong Shu is a Ph.D. candidate in computer science at New Jersey Institute of Technology in USA. She received her B.S. degree in information management and system from Peking University in China. Her research interests include big data workflow, parallel and distributed computing, cloud computing, energy efficiency, wireless networking and mobile computing.