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OAK: Restart- and Age-Aware Scheduling for Distributed Machine Learning on Shared GPU Clusters
Distributed machine learning increasingly runs on shared multi-tenant GPU clusters where contention and failures are routine. Goodput-driven schedulers maximise instantaneous throughput but treat accumulated waiting time and restart cost as second-class signals: long-waiting jobs are repeatedly defe
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