NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction. (arXiv:2203.08339v2 [cs.LG] UPDATED)

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 labels -- i.e., sufficient examples of all
possible behaviors, including …

arxiv datacenter learning lg negative prediction