Abstract:
We present a model that uses large-deviations and the Pareto probability distribution to model self-similar data in high-performance infrastructure, such as the one found on computational and data grids, transactional and computational clusters, and multimedia streaming. We also show how Principal Component Analysis can reduce dimensionality of data, such as the one produced by different types of problems, user preferences and behaviour, and resource popularity.

Biography:
Raul Ramirez-Velarde finished a Chemical & Industrial Engineering degree in May de 1988 at Instituto Tecnologico y de Estudios Superiores de Monterrey (ITESM), Campus Monterrey, Mexico. He finished a Master Degree on Science with specialty in Computer Science in July 1991. He has been full time professor at ITESM since August de 1991. He reached Associate Professor Level in 2003. He finished a Ph.D. degree at ITESM in February 2004 in Information Technology with specialty in Multimedia Distributed Systems.

At the moment, Raul Ramirez is involved in research on modeling, analysis and design of video servers as part of the ITESM's Chair on Stereoscopic Video, on high-performance open service-oriented architectures modeling and security as part of ITESM's Chair on Information Security, and research on educational technology as part of the E-LANE project (An @LIS European Aid Initiative).