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English(EN) A Two-Stage Forecasting System for CPU Workload Prediction in Private Clouds

新的两阶段系统使用 XGBoost 预测云 CPU 工作负载

研究人员开发了一个新颖的两阶段预测系统,旨在预测私有云环境中的 CPU 工作负载。该系统首先预测客户服务请求(每秒事务数或 TPS),然后利用该预测来估算未来的 CPU 工作负载。该模型利用 XGBoost 算法和自适应在线再训练策略来处理不断变化的云工作负载,在实际测试中,大多数应用的对称平均绝对百分比误差 (SMAPE) 低于 7%。 AI

影响 通过提高 CPU 工作负载预测的准确性,改善云环境中的主动资源管理和智能自动扩展。

排序理由 关于云 CPU 工作负载预测新预测系统的学术论文。[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 →

新的两阶段系统使用 XGBoost 预测云 CPU 工作负载

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关于云 CPU 工作负载预测新预测系统的学术论文。[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) · Ashir Javeed, Anton Borg, H{\aa}kan Grahn, Lars Lundberg, Dhyey Patel, Sogand Shirinbab ·

    私有云中用于 CPU 工作负载预测的两阶段预测系统

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