  Some faults in data center networks require hours to days to repair because
they may need reboots, re-imaging, or manual work by technicians. To reduce
traffic impact, cloud providers \textit{mitigate} the effect of faults, for
example, by steering traffic to alternate paths. The state-of-art in automatic
network mitigations uses simple safety checks and proxy metrics to determine
mitigations. SWARM, the approach described in this paper, can pick orders of
magnitude better mitigations by estimating end-to-end connection-level
performance (CLP) metrics. At its core is a scalable CLP estimator that quickly
ranks mitigations with high fidelity and, on failures observed at a large cloud
provider, outperforms the state-of-the-art by over 700$\times$ in some cases.
