arXiv Machine Learning By Ye Yuan, Song Wang, Hongxun Zhou, Ling Wang, Xin Luo

A Novel Approach to Temporal QoS Estimation via Extended Kalman Filter-Incorporated Latent Feature Analysis

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arXiv:2606. 23010v2 Announce Type: replace Abstract: Predicting temporal Quality of Service (QoS) data is critical for optimizing network services and rationalizing resource allocation in cloud computing and service-oriented systems.

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arXiv Machine Learning
Sep 4

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

A two-stage forecasting system is introduced for predicting CPU workload in private clouds. The model first forecasts customer service requests in Transactions Per Second (TPS) and then estimates future CPU usage from the TPS forecast, both stages using XGBoost within a cascaded architecture. Experiments on real private‑cloud traces show SMAPE below 7% for most applications, with the best case achieving an MAE of 0.7372 and an R² of 0.9185, and stable error accumulation over a 60‑step horizon.

By Ashir Javeed, Anton Borg, H{\aa}kan Grahn, Lars Lundberg, Dhyey Patel, Sogand Shirinbab