Digital Twin for Tuning of Server Fan Controllers Cooling of IT equipment consumes a large proportion of a modern data centrexe2x80x99s energy budget and is therefore an important target for optimal control. This study analyses a scaled down system of six servers with cooling fans by implementing a minimal data driven timeseries model in TensorFlowKeras, a modern software package popular for deep learning. The model is inspired by the physical laws of heat exchange, but with all parameters obtained by optimisation. It is encoded as a customised Recurrent Neural Network and exposed to the timeseries data via nstep Prediction Error Minimisation (PEM). The thus obtained Digital Twin of the physical system is then used directly to construct a Model Predictive Control (MPC) type regulator that executes in real time. The MPC is then compared in simulation with a selftuning PID controller that adjust its parameters online by gradient descent.

EnergyEfficient Task Distribution Using Neural Network Temperature Prediction in a Data Center The growing demand for computing resources leads to a serious problem of excessive energy consumption in data centers. In recent studies, energy consumption of both computing and cooling equipment is drawing attention. For improving the energy efficiency of cooling equipment such as computer room air conditioners (CRACs), it is neccesary to predict temperatures in data centers and to optimize thermal management in data centers. In this study, we propose a temperature prediction method for servers in a data center using a neural network. We used the prediction result for distributing task targeting temperaturebased load balancing. First, we conducted an experiment in a real data center to evaluate the prediction accuracy of the proposed method. We then simulated task distribution based on the predicted temperatures and compared the maximum CPU temperature with a nonpredictive approach. The results indicated that the proposed method can reduce future CPU temperatures successfully compared to the nonpredictive approach, though in exchange for high computational cost.