COLLOQUIUM ON COMPUTATIONAL SCIENCES AND INFORMATICS – Data Management Expert Discussion Seminar: Learning-based Cost Management for Cloud Databases – Dr. Olga Papaemmanouil

April 16, 2018 @ 4:30 pm – 5:45 pm
Exploratory Hall, Room 3301
Matthias Renz


Olga Papaemmanouil, Associate Professor
Department of Computer Science at Brandeis University

Data Management Expert Discussion Seminar:
Learning-based Cost Management for Cloud Databases

Monday, April 16, 4:30-5:45
Exploratory Hall, Room 3301


Cloud computing has become one of the most active areas of computer science research, in large part because it allows computing to behave like a general utility that is always available on demand. While existing cloud infrastructures and services reduce significantly the application development time, significant effort is still required by cloud data management applications to manage their monetary cost, for often this cost depends on  a number of decisions including but not limited to performance goals, resource provisioning and workload allocation. These tasks depend on the application-specific workload characteristics and performance objectives and today their implementation burden is left on application developers.  

We argue for a substantial shift away from human-crafted solutions and towards leveraging machine learning algorithms to address the above challenges.   These algorithms can be trained on application-specific properties and customized performance goals to automatically learn how to provision resources as well as schedule the execution of incoming query workloads with low cost. Towards this vision, we have developed WiSeDB, a  learning-based cost  management service for cloud-deployed data management applications. In this talk, I will discuss how WiSeDB leverages  (a) supervised learning to automatically learn cost-effective models for guiding query placement, scheduling, and resource provisioning decisions for batch processing, and (b) reinforcement learning  to offer low cost online processing solutions, while being adaptive to resource availability and decoupled from notoriously inaccurate performance prediction models.

Speaker Bio:  Dr. Papaemmanouil is an Associate Professor in the Department of Computer Science at Brandeis University. Her research interest lies in the area of data management with a recent focus on cloud databases, data exploration, query optimization and query performance prediction. She received her undergraduate degree in Computer Science and Informatics at the University of Patras, Greece in 1999. In 2001, she received her Sc.M. in Information Systems at the University of Economics and Business, Athens, Greece. She then joined the Computer Science Department at Brown University, where she completed her Ph.D in Computer Science at Brown University in 2008. She is the recipient of an NSF Career Award (2013) and a Paris Kanellakis Fellowship from Brown University (2002)