一、报告题目
New constructions of cooperative MSR codes: Reducing node size to $\exp(O(n))$
二、主讲人
Min Ye, Tsinghua-Berkeley Shenzhen Institute
三、时间地点
2020-12-04, 14:00-15:00,华岗苑东楼119报告厅
四、摘要
We consider the problem of multiple-node repair in distributed storage systems under the cooperative model, where the repair bandwidth includes the amount of data exchanged between any two different storage nodes. Recently, explicit constructions of MDS codes with optimal cooperative repair bandwidth for all possible parameters were given by Ye and Barg (IEEE Transactions on Information Theory, 2019). The node size (or sub-packetization) in this construction scales as $\exp(\Theta(n^h))$, where $h$ is the number of failed nodes and $n$ is the code length.
In this talk, we give new explicit constructions of optimal MDS codes for all possible parameters under the cooperative model, and the node size of our new constructions only scales as $\exp(O(n))$ for any number of failed nodes. Furthermore, it is known that any optimal MDS code under the cooperative model (including, in particular, our new code construction) also achieves optimal repair bandwidth under the centralized model, where the amount of data exchanged between failed nodes is not included in the repair bandwidth. We further show that the node size of our new construction is also much smaller than that of the best known MDS code constructions for the centralized model.
五、主讲人简介
Min Ye received his B.S. in Electrical Engineering from Peking University, Beijing, China in 2012, and his Ph.D. in the Department of Electrical and Computer Engineering, University of Maryland, College Park in 2017. He then spent two years as a postdoctoral researcher at Princeton University. Since 2019, he has been an assistant professor in the Data Science and Information Technology Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua Shenzhen International Graduate School, Shenzhen, China. He received the 2017 IEEE Data Storage Best Paper Award. His research interests include coding theory, information theory, differential privacy, and machine learning.
