Tencent Cloud Developer
Apr 12, 2019 · Cloud Computing
Predictive Modeling for Hot Migration in Cloud Computing Using Ensemble Machine Learning
The study introduces a voting ensemble of Random Forest, AdaBoost, and XGBoost to predict hot‑migration success in cloud environments, achieving 97.44% accuracy and cutting timeout failures by roughly 80%, while quantifying feature importance—primarily CPU, network traffic, and memory—to guide proactive resource allocation.
Hot migrationcloud computingensemble models
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