Argus: A Real-EKS Study of When Predicting Spot Interruptions Beats Simple Checkpointing

Abstract:Elastic Compute Cloud (EC2) Spot is 60% to 90% cheaper than On-Demand but can be reclaimed on just a 2-minute notice; for expensive multi-node training this loss can be severe, with one reclaim costing hours of synchronous progress. We build Argus, a Kubernetes operator, and ask empirically, on a CIFAR-10 testbed, when predicting interruptions beats simple checkpointing. Argus on real EKS survives a real Spot drain with a graceful SIGTERM checkpoint, resuming from epoch 8 and losing only the in-progress epoch. Alongside, we further find that in an 80-trial benchmark, the reactive-on-notice degrades toward no protection once interruption outpaces the fixed 2-minute notice, and predictive wasted compute is driven to zero, but with an oversized fixed lead it over-migrates so severely that at the fastest rate only one of five runs completes, while periodic is a strong ML-free baseline. A lead-time sweep turns the lead prediction into a guideline where a small lead suffices for zero waste, but excess lead is wasteful. The predictor built is advisory (a proxy label); real interruption labels and large-model-scale validation are future work.

Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)

arXiv:2609.39067 [cs.LG]

(or arXiv:2609.39067v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.39067 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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