Ningfang Mi (Northeastern University)学术报告

发布日期:2017-06-20 浏览次数: [字体: ]

 

报告题目:Scratch the surface of the enormous technical challenge that’s confronting the backend of clouds and datacenters
报告人:Ningfang Mi an Associate Professor in Department of Electrical and Computer Engineering (ECE) at Northeastern University
(我院讲座教授)
报告时间:6月23日下午3点
报告地点:S2-109
Bio: Ningfang Mi is an Associate Professor in Department of Electrical and Computer Engineering (ECE) at Northeastern University since 2009. Dr. Mi graduated with a B.S. in Computer Science from Nanjing University, China in 2000 and a M.S. in Computer Science from the University of Texas at Dallas in 2004. She received her Ph.D in Computer Science from the College of William and Mary in 2009.  Her research interests include cloud computing, big data processing, resource management, capacity planning, MapReduce/Hadoop scheduling, performance evaluation, simulation and virtualization. Dr. Mi was a recipient of the 2015 National Science Foundation (NSF) CAREER Award, the 2014 Air Force’s Young Investigator Research (YIP) Award and the 2010 IBM Faculty Award. She is the director of the Northeastern University Computer Systems Research Laboratory (NUCSRL) at Northeastern University.
“Amazon’s website is taking too long to load.” “The most popular YouTube video won’t stop buffering.” “Twitter is over capacity.” While these may not seem like a big deal to end users, they merely scratch the surface of the enormous technical challenge that’s confronting the backend of data centers and cloud computing. Nowadays, these large-scaled cluster systems have become an important part of contemporary computing environments. Everybody is moving their computing and data from desktops to large cluster systems, spanning from scientific computing clusters to commercial and military data centers. However, maintaining such large systems with high efficiency and high dependability at low cost is an inherently difficult problem as the complexity of these systems increases and the workflows to these systems are becoming dynamic and diverse. This requires new designs that are able to manage unplanned increases or bursts in user demands. Co-scheduling a large number of applications can further incur severe resource contention; different performance management solutions are needed to meet their varying resource and performance requirements. Therefore, in this talk, we will present our recent research work that focuses on how to leverage the knowledge of workload patterns to develop new techniques and tools for modeling, prediction, and resource management.