Computing with Geo-distributed Data
|Event Date:||June 7, 2017|
|Speaker Affiliation:||Associate Professor
Department of Computer Science and Engineering University of Minnesota
|Type:||Computer Engineering Research Area Seminar
|Location:||POTR 234, Fu Room|
|Contact Name:||Professors Saurabh Bagchi and Ananth Grama
|School or Program:||Electrical and Computer Engineering
Today, large quantities of data are generated from disparate sources such as users, servers, devices, and sensors dispersed around the globe. The need to store, combine and analyze this information in a meaningful and timely manner has resulted in the need for efficient geo-distributed analytics, creating tradeoffs in terms of cost, timeliness, and quality of results. In this talk, I will discuss the challenges of computing with geo-distributed data, and present some of our work on optimizing computation for such highly-distributed environments. I will present new scheduling algorithms we have developed to optimize aggregating data streams in a geo-distributed analytics setting. In addition, I will present Nebula: a dispersed edge computing infrastructure we are building at UMN, to enable geo-distributed data-intensive computing.
Abhishek Chandra is an Associate Professor in the Department of Computer Science and Engineering at the University of Minnesota. His research interests are in the areas of Operating Systems and Distributed Systems, with current focus on performance and resource management in Cloud computing, Data-intensive computing, and Mobile computing platforms. He received his B.Tech. in Computer Science and Engineering from IIT Kanpur, India, and M.S. and PhD in Computer Science from the University of Massachusetts Amherst. He currently serves on the Editorial Boards of IEEE TCC and Springer Cluster Computing journals. He is a recipient of the NSF CAREER Award and IBM Faculty Award, an ACM Dissertation Award nominee, and a co-author on multiple Best Paper/Poster Awards.