Thermal-aware Energy-efficient Workload Scheduling for Data Centers

Given the emerging needs of artificial intelligence in both commercial and consumer uses, the rapid increase of energy consumption has become a major concern for data centers. Workload scheduling plays a crucial role in reducing the energy costs associated with large compute-intensive workloads in data centers. However, there has not been much research on reducing the energy costs for GPU-intensive workloads specifically. In this paper, we proposed two thermal-ware scheduling algorithms (GPUThermalAwareMinTemp and GPUThermalAware) for GPU-intensive workloads. Through the integration of machine learning models into a modified version of the GPUCloudSimPlus simulator, we compare the performance of our algorithms with three baseline algorithms by simulating cloud computing environments using a subset of real-world workload traces from Alibaba PAI. Our experimental results show that, compared with the worst-case algorithms, the GPU thermal-aware scheduling algorithms achieved 4.72% lower average host minimum temperatures and saved total energy by 1.17%.