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NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction
[article]
2022
arXiv
pre-print
Datacenters execute large computational jobs, which are composed of smaller tasks. A job completes when all its tasks finish, so stragglers -- rare, yet extremely slow tasks -- are a major impediment to datacenter performance. Accurately predicting stragglers would enable proactive intervention, allowing datacenter operators to mitigate stragglers before they delay a job. While much prior work applies machine learning to predict computer system performance, these approaches rely on complete
arXiv:2203.08339v2
fatcat:rt3ucuks35de7b7nrwdlopzdeq