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arXiv Machine Learning By Xinhua Miao, Bowei Yang, Zhengong Cai

AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift

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AdaptLSTM is an adaptive online learning framework designed for cloud workload forecasting under distribution drift. It detects drift using validation‑calibrated thresholds and performs selective, targeted updates, achieving a large portion of the accuracy gains of naive online learning while reducing per‑step compute cost. Experiments on Alibaba and Container traces show significant efficiency improvements and error reductions compared to static models and classical drift detectors.

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