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In this work, modeling and control of a time-varying nonlinear system is studied. The modeling is under the Gaussian process regression (GPR) framework, which is a data-driven approach. To address he time-varying phenomenon which commonly exists in the manufacturing processes, an online GPR algorithm is adopted. Next, model predictive control (MPC) is designed based on the obtained GPR model which...
Temperature prediction can enhance datacenter thermal management towards minimizing cooling power draw. Traditional approaches achieve this through analyzing task-temperature profiles or resistor-capacitor circuit models to predict CPU temperature. However, they are unable to capture task resource heterogeneity within multi-tenant environments and make predictions under dynamic scenarios such as virtual...
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