Title: Optimizing Memory/Storage Systems for Big Data Applications
Abstract: Optimizing memory/storage is one of the most critical issues in big data systems as huge amount of data need to be stored/transferred/processed in memory and storage devices. In this talk, I will introduce our recent work in optimizing memory/storage systems for big data applications. In particular, I will present an approach by deeply integrating device and application to optimize flash-based key-value caching – one of the most important building blocks in modern web infrastructures and high-performance data-intensive applications. I will also briefly talk about the challenges and opportunities by utilizing the NVDIMM (Non-Volatile Dual In-line Memory Module) technologies to reduce the long I/O latency for big data workloads.
Bio: Zili Shao has been an Associate Professor with Department of Computing, The Hong Kong Polytechnic University, Hong Kong, since 2010. He received the B.E. degree in electronic mechanics from the University of Electronic Science and Technology of China, China, in 1995, and the M.S. and the Ph.D. degrees from the Department of Computer Science, University of Texas at Dallas, Texas, USA, in 2003 and 2005, respectively. His current research interests include embedded software and systems, storage systems and related industrial applications.
He is an associate Editor for IEEE Transactions on Computers, IEEE Transactions on CAD, ACM Transactions on Design Automation of Electronic Systems, ACM Transactions on Cyber-Physical Systems. He serves/served the technical program committees of many top conferences in the real-time embedded system field such as DAC and RTSS.