科研工作

香港科技大学陈凯教授的学术报告

来源:     发布日期:2022-09-14    浏览次数:

报告时间:2022年9月16日(周五)10:00–12:00

报告地点:腾讯会议:992-162-540

报告题目:Towards a Scalable and Efficient RDMA Networking for Datacenters

Abstract:As the datacenter networking migrates from 10Gb/s to 40/100Gb/s or beyond, the traditional software-based TCP/IP stack cannot keep up with the increasing network speed. Consequently, hardware-based RDMA (Remote Direct Memory Access), originally invented in HPC, is now experiencing a renaissance in Ethernet-based datacenters. However, RDMA relies on lossless network to take effect, which poses significant challenges before it can be well utilized in lossy datacenter networking. In this talk, I will overview the research works from the community in the past 8 years to address these challenges, as well as our efforts in the HKUST iSING Lab towards a scalable and efficient RDMA networking for datacenters.

报告摘要:随着数据中心网络从10Gb/s扩展到40/100Gb/s以上,传统的基于软件的TCP/IP协议栈已经跟不上网络速率的增长。因此,最初在HPC中发明的基于硬件的RDMA(远程直接内存访问)现在又在基于以太网的数据中心研究中复兴。然而,RDMA的生效依赖于无损网络,这对它能否在有损数据中心网络中得到很好的应用提出了很大的挑战。本次报告将概述过去8年科研人员为应对这些挑战所做的工作,以及港科大iSING实验室为在数据中心建立可扩展和高效的RDMA网络所做的努力。

BioKai Chen is a Professor at HKUST, and the Director of HKUST iSING Lab and HKUST-WeChat joint Lab on Artificial Intelligence Technology (WHAT Lab). He received his BS and MS from University of Science and Technology of China (USTC) in 2004 and 2007, and PhD from Northwestern University in 2012, respectively. His research interests include Data Center Networking, Machine Learning Systems, and Privacy-preserving Computing. His work has been published in various top venues such as SIGCOMM, NSDI, OSDI and TON, etc., including a SIGCOMM best paper candidate. He is the Steering Committee Co-Chair of APNet, serves on Program Committees of SIGCOMM, NSDI, CoNEXT, INFOCOM, etc., and Editorial Boards of IEEE/ACM Transactions on Networking, Big Data, and Cloud Computing.

报告人介绍:陈凯是香港科技大学教授,智能网络系统实验室(iSING Lab)和香港科技大学-微信人工智能技术联合实验室(WHAT Lab)主任。2004年和2007年分别在中国科学技术大学获得学士和硕士学位,2012年在西北大学获得博士学位。他的研究兴趣包括数据中心网络、机器学习系统和隐私保护计算。其工作曾在SIGCOMM、NSDI、TON等多个顶级会议发表,包括SIGCOMM最佳论文候选人。他是APNet指导委员会联合主席,在SIGCOMM、NSDI、INFOCOM等程序委员会任职,以及IEEE/ACM Transactions on Networking、Big Data和Cloud Computing的编委。

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