Cloud Telemetry Modeling via Residual Gauss-Markov Random Fields
Nicholas C. Landolfi, Daniel C. O’Neill, Sanjay Lall · 2021
Can probabilistic graphical models characterize cloud telemetry? This paper promotes the affirmative view. Cloud systems are large, connected, and dynamic. Consequently, databased techniques to model their telemetry are high-dimensional, spatial, and unsupervised. Undirected probabilistic graphical models seem natural, but remain unexplored. We discuss one way around the limitation that cloud measurements violate usual assumptions of normality, and give a tractable estimation procedure for a candidate data model. As a preliminary test, we fit the model and use it to detect and localize anomalies in a synthetic environment and for a small-scale software system.