Energy-Efficient and Reliability-Aware Data Management in Mobile Storage Systems
主讲人：
谢涛
Tao Xie
Associate Professor, Department of Computer Science, San Diego State University
【Profile】
Tao Xie received the PhD degree in computer science from the New Mexico Institute of Mining and Technology in 2006. He is currently a tenured associate professor in the Department of Computer Science at the San Diego State University, San Diego, California. His research interests are storage systems, high performance computing, cluster and Grid computing, parallel and distributed systems, security-aware scheduling, and real-time/embedded systems. He published more than 50 papers in premier international journals and conferences. He received National Science Foundation Faculty Early Career Award in 2009. He is a member of the IEEE.
【Abstract】
Highly reliable, high performance and energy-efficient storage systems are essential for mobile data-intensive applications such as remote surgery and mobile data center. Existing mobile storage systems generally consist of an array of independent small form factor hard disks connected to a host by a storage interface in a mobile computing environment. Although hard disks are cost-effective and can provide huge capacity and high-throughput, they have some intrinsic limitations such as long access latencies, high annual disk replacement rates, fragile physical characteristics, and energy-inefficiency. Compared with hard disk drives, flash memory based solid state disks (SSDs) are much more robust and energy-efficient, and can offer much faster access times. A major concern on current flash disk is its relatively higher price. This project develops a hybrid disk array system, which integrates small capacity flash disks with high capacity hard disk drives to form a robust and energy-efficient storage system for mobile data-intensive applications. In particular, an array of new data management techniques including energy-efficient data placement, self-adaptive and reliability-aware data redistribution, and self-triggered data replication for data-intensive mobile applications built on the hybrid disk array framework will be developed.
