  Inferring the root cause of failures among thousands of components in a data
center network is challenging, especially for "gray" failures that are not
reported directly by switches. Faults can be localized through end-to-end
measurements, but past localization schemes are either too slow for large-scale
networks or sacrifice accuracy. We describe Flock, a network fault localization
algorithm and system that achieves both high accuracy and speed at datacenter
scale. Flock uses a probabilistic graphical model (PGM) to achieve high
accuracy, coupled with new techniques to dramatically accelerate inference in
discrete-valued Bayesian PGMs. Large-scale simulations and experiments in a
hardware testbed show Flock speeds up inference by >10000x compared to past PGM
methods, and improves accuracy over the best previous datacenter fault
localization approaches, reducing inference error by 1.19-11x on the same input
telemetry, and by 1.2-55x after incorporating passive telemetry. We also prove
Flock's inference is optimal in restricted settings
