Enhancing Network Failure Mitigation with Performance-Aware Ranking
Pooria Namyar, Arvin Ghavidel, Daniel Crankshaw, Daniel S. Berger, Kevin Hsieh, Srikanth Kandula, Ramesh Govindan, Behnaz Arzani
摘要
Cloud providers install mitigations to reduce the impact of network failures within their datacenters. Existing network mitigation systems rely on simple local criteria or global proxy metrics to determine the best action. In this paper, we show that we can support a broader range of actions and select more effective mitigations by directly optimizing end-to-end flow-level metrics and analyzing actions holistically. To achieve this, we develop novel techniques to quickly estimate the impact of different mitigations and rank them with high fidelity. Our results on incidents from a large cloud provider show orders of magnitude improvements in flow completion time and throughput. We also show our approach scales to large datacenters.
The author contributed to this work while at Microsoft. 1 We use network-level mitigations and mitigations interchangeably. We briefly discuss application-level mitigations in §3.4.
2 Its auto-mitigation system uses local criteria to determine whether taking a fixed action is better than taking no action.
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