Chip Temperature-Based Workload Allocation for Holistic Power Minimization in Air-Cooled Data Center

Minimizing the energy consumption is a dominant problem in data center design and operation. To cope with this issue, the common approach is to optimize the data center layout and the workload distribution among servers. Previous works have mainly adopted the temperature at the server inlet as the optimization constraint. However, the inlet temperature does not properly characterize the server's thermal state. In this paper, a chip temperature-based workload allocation strategy (CTWA-MTP) is proposed to reduce the holistic power consumption in data centers. Our method adopts an abstract heat-flow model to describe the thermal environment in data centers and uses a thermal resistance model to describe the convective heat transfer of the server. The core optimizes the workload allocation with respect to the chip temperature threshold. In addition, the temperature-dependent leakage power of the server has been considered in our model. The proposed method is described as a constrained nonlinear optimization problem to find the optimal solution by a genetic algorithm (GA). We applied the method to a sample data center constructed with computational fluid dynamics (CFD) software. By comparing the simulation results with other different workload allocation strategies, the proposed method prevents the servers from overcooling and achieves a substantial energy saving by optimizing the workload allocation in an air-cooled data center.