The intellectual module analyzes requests sent from clients by means of supervised machine learning techniques based on the well-known ensemble of decision trees Random Forest, identifies clients who use business critical applications and optimizes QoS parameters. When data flow within the storage infrastructure no longer fits the pattern, the algorithm alters QoS settings accordingly and continues to monitor activity of clients. This analyzer can also be used to aid prefetch algorithms.
Lead of R&D department, RAIDIX LLC
Graduated from the faculty of Mathematics and Mechanics of St. Petersburg State University, PhD, associate professor in SPb State University of Aerospace Instrumentation. Previously worked in EMC reserach lab and Jointlab Samsung Advanced Institute of Technology.
Software developer in R&D, RAIDIX LLC
Graduated from the faculty of Mathematics and Mechanics of St. Petersburg State University in 2015. Engaged in research in storage system for multimedia.