Root-cause analysis throughmachine learning in the cloud

Abstract: It has been predicted that by 2021 there will be 28 billion connected devices and that 80% of global consumer internet traffic will be related to streaming services such as Netflix, Hulu and Youtube. This connectivity will in turn be matched by a cloudinfrastructure that will ensure connectivity and services. With such an increase in infrastructure the need for reliable systems will also rise. One solution to providing reliability in data centres is root-cause analysis where the aim is to identifying the root-cause of a service degradation in order to prevent it or allow for easy localization of the problem.In this report we explore an approach to root-cause-analysis using a machine learning model called self-organizing maps. Self-organizing maps provides data classification, while also providing visualization of the model which is something many machine learning models fail to do. We show that self-organizing maps are a promising solutionto root-cause analysis. Within the report we also compare our approach to another prominent approachs and show that our model preforms favorably. Finally, we touch upon some interesting research topics that we believe can further the field of root-cause analysis