Improving the energy efficiency of data center has become a research focus in recent years. Previous works commonly adopted the inlet temperature constraint to optimize the thermal environment in the data center. However, the inlet temperature-aware method cannot prevent the servers from over-cooling. To cope with this issue, we propose a thermal-aware workload allocation strategy with respect to the chip temperature constraint. In this paper, we conducted a comparative evaluation of the performance between the chip and inlet temperature-aware workload allocation strategies. The workload allocation strategies adopt a POD-based heat recirculation model to characterize the thermal environment in data center. The contribution of the temperature-dependent leakage power to server power consumption is also considered. We adopted a sample data center under constant-flow and variable-flow cooling air supply to evaluate the performance of these two different workload allocation strategies. The comparison results show that the chip temperature-aware workload allocation strategy prevents the servers from over-cooling and significantly improves the energy efficiency of data center, especially for the case of variable-flow cooling air supply.

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.

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Abstract.2 T control chart is widely used to monitor the multivariate process for quality improvement.In this paper, economic performance analysis on the SVSSI 2 T control chart is studied. Two statuses whether the process is in control or out of control are considered to define the states of Markov Chain. And a numerical example is provided to compare the performances of three control charts in terms of AATS and EL. The results show that the performance of SVSSI control chart is better than other charts. Further, the genetic algorithm is used to search the optimal solution of the SVSSI 2 T control chart.