Machine Learning: The Key Ingredient to the “Self Driving Data Center”

Machine learning as the key ingredient for making “self-driving data center” a reality: Workloads are moving away from traditional physical servers toward virtual, and cloud environments that are quite complex with many layers and end-points that constantly grow. This presentation will discuss how principals of Topological Behavior Analysis (TBA) as a derivative of Topological Data Analysis (TDA) as well as other principals of machine learning can be applied to operations data to analyze existing relationships and discover hidden inner relationships in complex virtual and cloud environments. It will also discuss how machine learning semi-supervised principals can be applied to address the need for a single, easy-to-use way to identify and resolve problems, explore infrastructure improvements, and tune the efficiency of operations in large in complex virtual and cloud environments ultimately delivering the vision of “self-driving data center”. Finally, what open source community driven efforts can help IT to spring board into the next generation data center management.