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This paper presents a dependency-aware autoscaling framework for serverless environments, integrating graph-based bottleneck identification, multi-model forecasting (MLP, LSTM, CNN) via a probabilistic ensemble, and cost-aware scaling control. Experiments show 99.88% prediction accuracy and reduced infrastructure costs.
Modal is enhancing OSS RL frameworks with delta compression and other techniques for training frontier open-weight models. The slime framework brings lossless delta sync to disaggregated training setups.