Cloud Computing Research at the RADLab - Present and Future Michael J. Franklin, UC Berkeley The Berkeley RADLab is a collaborative effort involving nearly a dozen faculty members and postdocs, several dozen students and fifteen industrial sponsors focused on cloud computing. The lab is in the final year of a five-year effort to develop the software infrastructure to enable rapid deployment of robust, scalable, data-intensive internet services. In this talk I will give an overview of the RADLab effort and do a deeper dive on two projects: PIQL, a performance insightful query language for interactive applications, and SCADS, a self-managing, scalable key value store. I will also give an overview of a new effort we are kicking off on next generation cloud computing architectures (tentatively called the "AMPLab" - for Algorithms, Machines, and People) focused on large-scale data analytics, machine learning, and hybrid cloud/crowd computing. In a nutshell, if the RADLab approach has been to use Statistical Machine Learning in the service of building large-scale systems, the AMPLab will explore using large-scale systems to support Statistical Machine Learning and other data analysis techniques, as well as to better understand how to support collaborative efforts of huge populations of users connected through cloud resources.