Energy-saving optimization of server multi-fan control system based on weighting mechanism and evolution strategy

• Energy-Saving Optimization of Server Multi-Fan Control System Based on Weighting Mechanism and Evolution Strategy • Different from most server fan control literature that emphasizes syn- chronous fans modulation, this study proposes the weighting mecha- nism for asynchronous fans modulation. In this method, multiple fans in a server are modulated independently by the fan weighting. Utiliz- ing appropriate weightings for asynchronous fans modulation to meet required thermal specifications for server components while also effec- tively reducing the total fans power for fan control. • This research combines the weighting mechanism with the Evolution- ary Strategy (ES) algorithm and introduces specially designed fitness functions to establish the multi-fan control system for server thermal control. The developed system can identify approximately optimal weightings within a limited number of searches based on various server loading conditions. By modulating fans asynchronously using these optimal weightings, the system complies with thermal specifications while also achieving approximately optimal energy savings. In exper- iments designed for eight different server loading scenarios, the sys- tem saves an average of 43.1% in total fans power when compared to synchronous fans modulation. Additionally, it only requires searching 14.2% of all possible weightings’ options. Furthermore, the probabil- ity of each approximately optimal weighting corresponding to a global optimal solution is 47.5%. • The system proposed in this study eliminates the need for constructing thermal models or analyzing extensive datasets, thereby reducing sys- tem deployment costs. In the future, adjustments to the internal code parameters will allow for its application to a variety of server types, including those with different numbers of fans. Each server in data centers is equipped with multiple fans and heat-generating components. Factors such as fans speed, layout of components, and loading scenarios, will influence the airflow and thermal flow fields within a server. Hence, a server thermal control is classified as a Multi-Input Multi-Output (MIMO) nonlinear control system, which cannot be analyzed and controlled using simple linear system method. By using the weighting mechanism of different weighting values for each cooling fan to asynchronously modulate multiple fans, and combining the Evolutionary Strategy (ES) algorithm with designing fitness functions, this study realizes the multi-fan thermal control system for a server. The developed system can attain approximately optimal weightings within a limited number of searches, based on different loading scenarios. By modulating fans asynchronously with these weightings, the system can achieve approximately optimal energy-saving while still meet the thermal specifications in a server; that is the allowed highest temperature of CPU and PCIe. Compared to modulating fans synchronously, the multi-fan control system modulating fans asynchronously saves an average of 43.1% of the total fans power, and only need to search 14.2% of all weighting options under the eight designed loading scenarios for experiments. Furthermore, the probability of each approximate optimal weighting corresponding to global optimal solution is 47.5%. Experimental results demonstrate that the pro- posed asynchronously modulating multi-fan control system can simultaneously satisfy the thermal specifications and achieves approximately optimal energy-saving for a server. As a result, the developed system is feasible with excellent performance for significant energy saving, while it is no need to construct a mathematical thermal models or to analyze numerous datasets. In the future, by adjusting the code parameters of system, it may be to be applied to various types of servers. Energy-Saving Optimization of Server Multi-Fan Control System Based on Weighting Mechanism and Evolution Strategy