School of EECS Seminar: Fast Accesses in Memory and Storage in Big Data Environment
Professor Xiaodong Zhang
Ohio State University
November 9, 2018
Patrick F. Taylor Hall, Room 3107
A major goal of algorithms analysis and implementation in data processing is to read
and write data records from memory or storage in high speed at a low cost for a given
data storage format. As the data volume generated in the society continues to grow
in an increasingly rapid way, we have reevaluated several commonly used data accessing
methods including LSM-tree for sequentially archived data, and storing/retrieving
methods for key-value stored data.
In this talk, I will show their limits and inabilities to handle big volume of data in a scalable way. I will also present three new research results: (1) re-enabling buffer caching capability for LSM-tree to achieve high performance of both reads and writes to process sequentially archived data, (2) balancing both network bandwidths and storage transfers for relational tables in large clusters, and (3) maximizing throughput of in-memory key-value stores by GPUs. All the related algorithms and software implementations are open sourced, some of which have been adopted in production systems.
Xiaodong Zhang is the Robert M. Critchfield Professor in Engineering at the Ohio State University. His research interests focus on data management in computer and distributed systems. He has made strong efforts to transfer his academic research into advanced technology to update the design and implementation of major general-purpose computing systems. He received his Ph.D. in Computer Science from University of Colorado at Boulder, where he received Distinguished Engineering Alumni Award in 2011. He received Lutron Foundation's Education Leadership Award for his contributions as the Department Chair of Computer Science and Engineering, 2006-2018. He is a Fellow of the ACM, and a Fellow of the IEEE.