Online Identification of IT Systems through Active Causal Learning
This addresses the challenge of automating network and system management for IT engineers by providing a principled approach to replace expert-designed models, though it is incremental as it builds on existing causal learning techniques.
The paper tackles the problem of automatically identifying causal models of IT systems, which are crucial for tasks like prediction and diagnosis, by introducing an online, data-driven method called active causal learning that uses Gaussian process regression and an intervention policy; experimental results show it achieves accurate model identification with low operational interference.
Identifying a causal model of an IT system is fundamental to many branches of systems engineering and operation. Such a model can be used to predict the effects of control actions, optimize operations, diagnose failures, detect intrusions, etc., which is central to achieving the longstanding goal of automating network and system management tasks. Traditionally, causal models have been designed and maintained by domain experts. This, however, proves increasingly challenging with the growing complexity and dynamism of modern IT systems. In this paper, we present the first principled method for online, data-driven identification of an IT system in the form of a causal model. The method, which we call active causal learning, estimates causal functions that capture the dependencies among system variables in an iterative fashion using Gaussian process regression based on system measurements, which are collected through a rollout-based intervention policy. We prove that this method is optimal in the Bayesian sense and that it produces effective interventions. Experimental validation on a testbed shows that our method enables accurate identification of a causal system model while inducing low interference with system operations.