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Invited keynote speakers

Monday, April 22, 2013

Prashant Shenoy, University of Massachusetts School of Computer Science

Prashant Shenoy


As online services become increasingly common, the complexity of backend distributed server applications in data centers has also continued to grow. At the same time, there is an increasing need to enhance the manageability of these large applications by automating common management tasks, which requires a good understanding of the run-time behavior of the application under different scenarios. However, the rising complexity of these applications makes the tasks of manually modeling and analyzing their run-time behavior increasingly difficult. In this talk, I will argue for the need to automate the modeling of the run-time performance of distributed data center applications. I will present techniques for automatically deriving application models using statistical methods from machine learning. I will describe how we have put these ideas into practice into two systems that we have built, Modellus and Predico, and will present case studies of using these systems for management tasks such as capacity planning and what-if analysis of data center applications.


Prashant Shenoy is currently a Professor of Computer Science at the University of Massachusetts Amherst. He received the B.Tech degree in Computer Science and Engineering from the Indian Institute of Technology, Bombay and the M.S and Ph.D degrees in Computer Science from the University of Texas, Austin. His research interests lie in distributed systems and networking, with a recent emphasis on cloud and green computing. He has been the recipient of the National Science Foundation Career Award, the IBM Faculty Development Award, the Lilly Foundation Teaching Fellowship, and the UT Computer Science Best Dissertation Award, and several best paper awards at leading conferences. He serves on editorial boards of the ACM Transactions on the Web and the Multimedia Systems journal and has served as the program chair for ACM Multimedia, ACM Sigmetrics, World Wide Web, Performance, Multimedia Computing and Networking, IEEE Comsnets, and Hotcloud . He is a distinguished member of the ACM and a fellow of the IEEE.

The presentation slides are available at

Tuesday, April 23, 2013

Len Bass, NICTA, Australia

Len Bass


Wednesday, April 24, 2013

Yuqing Gao, IBM T. J. Watson Research Center, US

Yuqing Gao


In the internet and mobile era, enterprises are facing both the challenge and business opportunities that introduced by Big Data, which has the characteristics of high volume, high velocity, and high variety. Big Data and the emergence of Internet-facing transactional workloads will blur the separation between traditional transactional and analytics workloads. To extract business value and make actionable insight from the unprecedented volume of the data at a rate up to 10,000 times faster than traditional systems, data centric computing becomes necessary to prevent the movement of Big Data that can often swamp the actual processing time for the application, dominating performance cost. In this talk, I will talk those technologies that are recently built at IBM Research to address challenges in design data centric computing systems, particularly, in the area of data access latency, data ingestion, and massive scale-out distributed caching in the exemplary context of an online product recommendation application. I will talk an innovative distributed caching system that exploits low latency interconnects to utilize hash maps of data keys on each server for local lookup while data resides and are accessed across clustered systems. The distributed cache can achieves 100 to 1000-fold performance gain over many caching methods.