Speaker
Maksim Gubin
(Tomsk Polytechnic University)
Description
Every modern scientific experiment deals with the processing of large amounts of experimental data (up to exabytes) employing millions of computing processes and delivering corresponding scientific payloads. It raises the task of the efficient management of the batch processing of payloads, that should consider the well-defined grouping mechanism.
Understanding naturally occurring groupings of payloads would increase the processing rate and improve the scheduling. The automated discovery of the payloads groups should consider not only the descriptive parameters of the payload itself but the characteristics of its interaction processes with computing resources and the computing environment.
Our work is focused on applying machine learning methods to solve the stated problem, and particularly to evaluate and to use the approach of cluster analysis. Besides the ultimate goal, it will benefit the other related analytical services and processes aimed at analyzing particular payload parameters (e.g., prediction process) and/or set of parameters (e.g., correlations discovery).
Primary author
Maksim Gubin
(Tomsk Polytechnic University)
Co-authors
Maria Grigoryeva
(National Research Centre «Kurchatov Institute»)
Mikhail Titov
(National Research Centre «Kurchatov Institute»)
