'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
A crucial step in remedying faults within network infrastructures is to determine their root cause. However, the large-scale, complex and dynamic nature of modern networks makes causal inference-based root cause analysis challenging in terms of scalability and knowledge drift over time. In this paper, we propose a framework that utilises the neuroscientific concept of functional connectivity– a graph representation of statistical dependencies between events– as a scalable approach to acquire and maintain prior knowledge for causal inferencebased RCA approaches in dynamic networks. We demonstrate on both synthetic and real-world data that our proposed approach can provide significant speedups to existing causal inference approaches without significant loss of accuracy. We show that, in some cases, such prior knowledge can even improve the accuracy of causal inference. Finally, we discuss the impact of the choice of user-defined parameters on causal inference accuracy and conclude that the framework can safely be deployed in the real world
