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Some faults in data center networks require hours to days to repair becausethey may need reboots, re-imaging, or manual work by technicians. To reducetraffic impact, cloud providers \textit{mitigate} the effect of faults, forexample, by steering traffic to alternate paths. The state-of-art in automaticnetwork mitigations uses simple safety checks and proxy metrics to determinemitigations. SWARM, the approach described in this paper, can pick orders ofmagnitude better mitigations by estimating end-to-end connection-levelperformance (CLP) metrics. At its core is a scalable CLP estimator that quicklyranks mitigations with high fidelity and, on failures observed at a large cloudprovider, outperforms the state-of-the-art by over 700$\times$ in some cases.

Mitigating the Performance Impact of Network Failures in Public Clouds