报告人： 李钧 (Jun Li), PhD Assistant Professor, Florida International University, USA
时间：2018 年 8 月 9 日下午 15:00-
地点： 复旦大学张江校区计算机楼 405 会议室
联系人： 王新 firstname.lastname@example.org
Distributed storage systems store a substantial amount of data in a large number of servers built with commodity hardware. In order to protect data against server failures, erasure coding has been deployed in many distributed storage systems because of its low storage overhead. In particular, since disk I/O is, in many cases, a bottleneck in the distributed storage system, locally repairable codes, have been proposed that incur low volumes of disk I/O when reconstructing missing data after server failures. However, since original data can only be read from specific servers, existing designs of locally repairable codes suffer from limited data parallelism. Besides, if the performance of servers is heterogeneous, slow servers may become the bottleneck when accessing data in parallel. In this talk, we present our work on Galloper codes, a novel family of locally repairable codes, that achieve low disk I/O during reconstruction and meanwhile extend data parallelism from specific servers to all servers. Moreover, the amount of original data in each server can be arbitrarily determined based on the performance of corresponding servers. We have implemented a prototype of Galloper codes on Apache Hadoop, and our experimental results have shown that Galloper codes can reduce the completion time of MapReduce jobs by up to 42.9%, with a comparable performance as existing locally repairable codes, in terms of disk I/O overhead, as well as encoding and reconstruction overhead.
Jun Li is an Assistant Professor in the School of Computing and Information Sciences, Florida International University. He received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of Toronto, in 2017, and his B.S. and M.S. degrees from the School of Computer Science, Fudan University, China, in 2009 and 2012. His research interest focuses on large-scale distributed storage and computing systems with erasure coding. Merging the gap between theory and practice, his research studies both theoretical and practical challenges of deploying erasure coding in distributed storage and computing systems with high performance and low resource consumption.