Towards Optimal Sensor Placement for Hot Server Detection in Data Centers

Recent studies have shown that a significant portion of the total energy consumption of many data centers is caused by the inefficient operation of their cooling systems. Without effective thermal monitoring with accurate location information, the cooling systems often use unnecessarily low temperature set points to over cool the entire room, resulting in excessive energy consumption. Sensor network technology has recently been adopted for data-center thermal monitoring because of its non-intrusive nature for the already complex data center facilities and robustness to instantaneous CPU or disk activities. However, existing solutions place sensors in a simplistic way without considering the thermal dynamics in data centers, resulting in unnecessarily degraded hot server detection probability. In this paper, we first formulate the problem of sensor placement for hot server detection in a data center as a constrained optimization problem. We then propose a novel placement scheme based on Computational Fluid Dynamics (CFD) to take various factors, such as cooling systems and server layout, as inputs to analyze the thermal conditions of the data center. Based on the CFD analysis in various server overheating scenarios, we apply data fusion and advanced optimization techniques to find a near-optimal sensor placement solution, such that the probability of detecting hot servers is significantly improved. Our empirical results in a real server room demonstrate the detection performance of our placement solution. Extensive simulation results also show that the proposed solution outperforms a commonly used placement solution in terms of detection probability.