The Enterprise and Big Data Systems: Yesterday, Today, and Tomorrow

Processing ever-increasing amounts of information and providing a meaningful analysis of large datasets (Big Data) has become a significant computing challenge in the Enterprise environment. New tools, frameworks, and systems have been proposed for Big Data processing. They target a variety of data (everything from business transactions to sensor data to tweets) and aim to offer new useful insights via advanced real-time analytics and/or batch-driven data analysis. The common theme of these underlying systems is that they represent a scale-out approach on commodity machines. Using a MapReduce framework I will present and analyze challenges in performance management of such systems. I will talk about the community and enterprise efforts to design unified and/or integrated data processing frameworks that aim to simplify application development and enhance data analytics. Finally, I will discuss hardware and resource usage patterns imposed by modern and emerging scale-out applications and their possible impact on the future system design.

Dr. Ludmila Cherkasova is a principal scientist in the Systems Research Lab, at Hewlett-Packard Labs, Palo Alto, USA. Her research interests include the analysis, design, and management of concurrent and distributed systems (such as emerging systems for Big Data processing, internet and media applications, virtualized environments, and next generation data centers). She has authored over 100 referred publications and more than 70 patent applications. Over the years she has mentored and co-advised more than 15 PhD students/interns. She has served as a Track/PC co-chair of 6 International conferences. She is an ACM Distinguished Scientist and recognized by the Certificate of Appreciation from the IEEE Computer Society. She has earned 6 Best Paper awards. Her most recent works were on the design of performance analysis and optimization techniques for MapReduce environments.