Optimizing thermal design of data center cabinets with a new multi-objective genetic algorithm
Distributed and Parallel Databases
A Measurement-Based Method for Improving Data Center Energy Efficiency
SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
A survey on wireless mesh networks
IEEE Communications Magazine
Green data centers and hot chips
Proceedings of the 46th Annual Design Automation Conference
Utility-function-driven energy-efficient cooling in data centers
Proceedings of the 7th international conference on Autonomic computing
Towards data center self-diagnosis using a mobile robot
Proceedings of the 8th ACM international conference on Autonomic computing
A robot as mobile sensor and agent in data center energy management
Proceedings of the 8th ACM international conference on Autonomic computing
A unified approach to coordinated energy-management in data centers
Proceedings of the 7th International Conference on Network and Services Management
State-of-the-art research study for green cloud computing
The Journal of Supercomputing
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The combination of rapidly increasing energy use of data centers (DCs), which is triggered by dramatic increases in IT (information technology) demands, and increases in energy costs and limited energy supplies has made the energy efficiency of DCs a central concern from both a cost and a sustainability perspective. This paper describes three important technology components that address the energy consumption in DCs. First, we present a mobile measurement technology (MMT) for optimizing the space and energy efficiency of DCs. The technology encompasses the interworking of an advanced metrology technique for rapid data collection at high spatial resolution and measurement-driven modeling techniques, enabling optimal adjustments of a DC environment within a target thermal envelope. Specific example data demonstrating the effectiveness of MMT is shown. Second, the static MMT measurements obtained at high spatial resolution are complemented by and integrated with a real-time sensor network. The requirements and suitable architectures for wired and wireless sensor solutions are discussed. Third, an energy and thermal model analysis for a DC is presented that exploits both the high-spatial-resolution (but static) MMT data and the high-timeresolved (but sparse) sensor data. The combination of these two data types (static and dynamic), in conjunction with innovative modeling techniques, provides the basis for extending the MMT concept toward an interactive energy management solution.