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 real-time 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.

Cooling system is the second most energy-consuming part of a modern data center; herewith it often consumes energy inefficiently. Therefore, any possibility allowing to reduce energy consumption of cooling systems should be studied and put into practice if successful. In this paper, the idea about impact of IT load distribution between servers on energy consumption of data center cooling system is proposed. The idea is in distribution of total IT load between servers according to the location of the servers inside racks. To test idea, the model simulating the thermal behaviour and energy consumption of a real data center located in Northern Sweden is developed. Comparing the data obtained from the real plant and the data generated at the simulation shows the reliability of the model. Two strategies of IT load distribution between servers are considered, and their impact on energy consumption of cooling system is demonstrated.

Abstract- The main goal of climate control systems in data centres is to keep the temperature and humidity in a suitable level for computational devices. Therefore, cooling and humidification systems are essential parts of every Building Automation System (BAS), which is utilized in server rooms. Although the current climate control systems ensure appropriate thermal conditions to computational nodes such as servers, they waste substantial amount of energy. The main cause of this inefficiency is that the current climate control systems, which are responsible for thermal management of the data centres, follow rigid control strategies that maintain constant thermal conditions irrespective of climate changes caused by various computational loads in the plant. To address this issue, in our previous works we proposed a method of optimizing energy consumption in data centre cooling systems while maintaining an acceptable level of thermal comfort for CPUs in the server room. In this paper, we present the enhancement of our previous method by incorporating humidity control into it. The enhanced method consists of a thermal model of server room, and a simulation tool to find an energy efficient control strategy for the climate control system in different situations by comparing different control strategies. The effectiveness of the proposed method has been investigated via simulation and the result, which shows 41.5% reduction in total energy consumption is presented.

Modern data centers in many aspects are akin to industrial plants that generate a lot of heat by consuming substantial amount of energy and require powerful cooling and ventilation. Cooling system contributes with 30 to 50% of the total energy consumption of data centers. An effective way to address energy efficiency in such cooling systems is to apply advanced automation solutions, similar to that of industrial and building automation systems. However, existing automation solutions are not flexible enough to meet requirements of cooling systems in modern data centres. This paper is an endeavour to utilize distributed adaptive automation architecture in order to improve energy efficiency of cooling. The proposed automation algorithms are validated in a simulation environment which models the thermal behaviour of a server room and helps to find the most energy efficient control strategy for controlling thecooling devices. This paper describes the simulation tool comprising of thermal behaviour modelling in MATLAB/SIMULINK connected in closed-loop with the distributed control environment of IEC 61499 standard. Simulation of a typical server room under certain constraints using the proposed tool is described and the results are presented. The results demonstrate the potential of improving higher energy efficiency, flexibility and better decision-making ability for controlling the cooling systems.

Cooling is an extremely important process in modern data centers. Cooling systems of server rooms ensure appropriate operation conditions to IT systems, such as servers and data storage, but, on the other side, they consume a lot of energy. Current control systems, which are installed in data centers and are responsible for thermal management of the facilities, are following conservative control strategies that maintain constant thermal conditions irrespective of computer load and outside temperature, thus efficient use of energy has not been appropriately addressed by them. In this paper, a method of optimizing energy consumption while maintaining an acceptable level of thermal comfort for CPUs in the server room is proposed.In the proposed method, a behavioral thermal model for serverroom should be created first, and then thermal behavior of theserver room would be simulated under different circumstancesusing its thermal model, in order to find an optimum controlstrategy capable of retaining balance between thermal comfortand efficient use of energy. The effectiveness of the proposedmethod has been investigated via simulating a typical serverroom using MATLAB and SIMULINK and the results aredemonstrated.