Robustness of Causal Discovery Algorithms: a Testbed Study on NFV Systems

We explore the applicability to high-speed NFV systems of causal discovery, a framework of statistical and algorithmic techniques that aims to uncover causal relationships. This framework highlights the ‘true’ structure of the processes that lead to observed outcomes while transcending spurious correlations. Causal discovery is crucial in the NFV domain, where introducing new levels of abstraction in the execution of virtualized services may diminish an observer’s ability to understand configuration or runtime issues. As a drawback, however, strict assumptions must be established concerning the data collection and the underlying system behavior. Most causal discovery techniques have been exercised on synthetic data, which lack the complexity and subtlety of real-world data generation processes. In this paper, we instrument a testbed to allow the controlled deployment and perturbation of NFV topologies and evaluate the algorithms’ robustness, defined as their ability to successfully reconstruct a correct configuration from observational and interventional probings. We then consider some ramifications of discovery quality for anomaly detection.