In this talk, I will introduce two examples of our data-informed cloud system analytics projects in IBM Research - Almaden.
The first is a lightweight, non-instrusive, and data-driven blackbox storage performance modeling framework for efficient cloud storage management.
The second is a big data analytics platform in production which catches outliers from millions of enterprise backup jobs each day.
I will also discuss several major research efforts ongoing in IBM Research - Almaden.
Yang Song is a Research Staff Member and Data Science Lead in the Cloud System Analytics Research Department at IBM Almaden Research Center.
His research interests include statistical modeling and analysis, optimization, machine learning, big data technologies, and their applications in cloud environments for efficient resource allocation and management.
He has served on the organizing and technical program committees of many conferences and the editorial board of the IEEE Transactions on Vehicular Technology.
He received the IEEE Communication Society (ComSoc) Best Young Professional Industry Award in 2015. Prior to joining IBM Research, he obtained his Ph.D. degree in the University of Florida.