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New Two-Stage System Predicts Cloud CPU Workload Using XGBoost

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel two-stage forecasting system designed to predict CPU workload in private cloud environments. This system first forecasts customer service requests (Transactions Per Second or TPS) and then uses that prediction to estimate future CPU workload. The model utilizes the XGBoost algorithm and an adaptive online retraining strategy to handle evolving cloud workloads, achieving a Symmetric Mean Absolute Percentage Error (SMAPE) below 7% for most applications in real-world tests. AI

IMPACT Improves proactive resource management and intelligent auto-scaling in cloud environments by enhancing CPU workload prediction accuracy.

RANK_REASON Academic paper detailing a new forecasting system for cloud CPU workload prediction. [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 →

New Two-Stage System Predicts Cloud CPU Workload Using XGBoost

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Academic paper detailing a new forecasting system for cloud CPU workload prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ashir Javeed, Anton Borg, H{\aa}kan Grahn, Lars Lundberg, Dhyey Patel, Sogand Shirinbab ·

    A Two-Stage Forecasting System for CPU Workload Prediction in Private Clouds

    arXiv:2609.03457v1 Announce Type: new Abstract: Accurate cloud resource forecasting is essential for proactive resource provisioning, maintaining Quality of Service (QoS), and reducing operational costs in dynamic cloud environments. The existing forecasting approaches predominan…

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