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AdaptLSTM framework offers efficient online learning for cloud workload forecasting

By PulseAugur Editorial · [1 sources] ·

Researchers have developed AdaptLSTM, a novel framework for online learning designed to efficiently forecast cloud workloads amidst changing data distributions. This method selectively updates models when drift is detected, significantly reducing computational costs compared to naive online learning approaches. AdaptLSTM demonstrates superior efficiency and accuracy on benchmark datasets like Alibaba Machine Trace and Container Trace, outperforming traditional drift detection methods and matched-budget baselines. AI

IMPACT Improves efficiency of online learning for time-series forecasting in cloud infrastructure.

RANK_REASON Academic paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AdaptLSTM framework offers efficient online learning for cloud workload forecasting

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Academic paper detailing a new machine learning framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xinhua Miao, Bowei Yang, Zhengong Cai ·

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

    arXiv:2610.12265v1 Announce Type: new Abstract: Accurate workload forecasting is critical for elastic resource provisioning in web-scale cloud services, where distribution shifts driven by viral content, product launches, and user behavior degrade offline-trained models rapidly. …

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