Scheduled Sampling for Recursive Multi-Step GPU Temperature Forecasting

Energy-efficient workload management is increasingly important in data centers as demand for cloud computing continues to grow. Recent findings in workload management point to thermal-aware load balancers as an effective approach to reduce energy consumption via cooling costs. Due to the high energy cost of Data Centers (DCs) and possible risk of disrupting critical services, simulations can be used to evaluate temperature-aware load-balancing algorithms. In this paper, we leverage an existing DC simulator's environmental parameters for integrating a temperature prediction model to construct a temperature-aware workload distribution simulation. Using scheduled sampling, we implemented a custom recursive multi-step forecasting model that reduced the discrepancy between training error and simulation predictions. Compared with a baseline GPU temperature prediction model, our hybrid CNN-LSTM model achieved a reduction in RMSE from 2.54 to 0.73.