Evaluating the Robustness of Causal Discovery Algorithms with Observations and Interventions in VNF Deployments

Tianzhu Zhang, Davide Rinaldi, Fabio Pianese, Armen Aghasaryan

Keywords: Causal discovery, Network Function Virtualization, Performance evaluation, Testbed

Abstract: Causal discovery (CD) incorporates a large collection of interdisciplinary research endeavors from statistics, computer science, and philosophy to uncover the true causal relationship from data and move beyond mere correlations to expose the underlying data generation mechanism. Despite the rich set of causal discovery algorithms, they also bear some common limitations, including demanding assumptions and lack of validation using real-world data, making their applicability in real systems questionable. This paper explores the practical challenges of performing causal discovery in real systems. We construct a controllable Network Function Virtualization (NFV) system that allows the deployment and perturbation of interconnected topologies of high-performance Virtual Network Functions (VNFs). Our contribution is a comparison of the ability of state-of-the-art CD algorithms to reconstruct the correct causal configuration from data in observational and interventional settings.

Submission Number: 19