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English(EN) AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift

AdaptLSTM框架为云工作负载预测提供高效在线学习

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了AdaptLSTM，一种新颖的在线学习框架，旨在应对数据分布变化的情况下高效预测云工作负载。该方法在检测到漂移时选择性地更新模型，与朴素的在线学习方法相比，显著降低了计算成本。AdaptLSTM在Alibaba Machine Trace和Container Trace等基准数据集上展示了卓越的效率和准确性，其表现优于传统的漂移检测方法和匹配预算的基线模型。 AI

影响 提高了云基础设施中时间序列预测的在线学习效率。

排序理由 详细介绍新机器学习框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AdaptLSTM框架为云工作负载预测提供高效在线学习

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详细介绍新机器学习框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    AdaptLSTM：云工作负载分布漂移下的高效自适应在线学习预测

    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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