A hybrid fault detection and diagnosis method in server roomsxe2x80x99 cooling systems Data centers as all complex systems are prone to faults, and cost of them can be very high. This paper is focused on detecting the faults in the cooling systems, in particular on local fans level. In the paper, a hybrid approach is proposed. In the approach a model is used as substitute of the real system to generate dataset containing records of both normal and fault cases. On the generated data, machine learning algorithm or ensemble of algorithms are selected and trained to detect the faults. To demonstrate the approach, the rack model of real data center is created, and reliability of the model is shown. Using the model, the dataset with normal as well as abnormal records of data is generated. To detect faults of local fans, simple classifiers are built for all pairs: a local fan  a processor unit. Classifiers are trained on one part of generated data (training data), and then their accuracy is estimated on another part of generated data (test data). A realtime fault detection system is built based on the classifiers. The rack model is used as the substitute of the real plant to check operability of the system.

Bridging Server and Cooling: Toward Effective Energy Management in Data Centers This paper studies the energy management problem in data centers. As the most energyconsuming subsystems, server and cooling have been considered as the key of energy saving for data center. However, due to the fact that servers are not directly connected to cooling devices, it is not trivial to optimize them jointly. In this paper, we formulate the joint server and cooling energy management problem and analyze its challenges, and design a Learningbased Energy management Scheme (LEES) to solve the joint energy management problem. Firstly, machine learning methods are investigated in the cooling power modeling and cabinet temperature modeling. Based on the models, a simple yet efficient energy management scheme is proposed to find an optimized configuration of server load distribution, server status and air supply temperature setting for the data center to reduce the total energy consumption. Extensive simulation results demonstrate that our method is able to reduce the total energy consumption of data centers significantly.