Abstract:

Rising complexity in systems management remains an open problem. As complexity increases, so do the costs associated with operating large-scale computing environments.

One approach for addressing these issues is to build self-healing systems that can autonomously detect and recover from faults. Such approaches combine machine learning with closed control loops to reduce the number of situations requiring human involvement. By reducing the need for human interaction, operational costs are reduced and systems complexity is reduced.

This talk will provide an overview of current self-healing systems methodologies (i.e., frameworks) and briefly discuss an unsupervised methodology for detecting systems faults.

Bio:

Chris Schneider is a second year Ph.D. student under Prof. Simon Dobson and Dr. Adam Barker. Before attending the University of St Andrews he completed an M.Sc. in Security Informatics at The Johns Hopkins University, and worked in industry as a Security Technologist.