On building next generation data centers: energy flow in the information technology stack.
ABSTRACT The demand for data center solutions with lower total cost of ownership and lower complexity of management is driving the creation of next generation datacenters The information technology industry is in the midst of a transformation to lower the cost of operation through consolidation and better utilization of critical data center resources. Successful consolidation necessitates increasing utilization of capital intensive "always-on" data center infrastructure, and reducing the recurring cost of power. A need exists, therefore for an end to end methodology that can be used to design and manage dense data centers and determine the cost of operating a data center. The chip core to the cooling tower model must capture the power levels and thermo-fluids behavior of chips, systems, aggregation of systems in racks, rows of racks, room flow distribution, air conditioning equipment, hydronics, vapor compression systems, pumps and heat exchangers. Earlier work has outlined the foundation for creation of a "smart" data center through use of flexible cooling resources and a distributed sensing and control system that can provision the cooling resources based on the need. This paper shows a common platform which serves as an evaluation and basis for policy based control engine for such a "smart" data center with much broader reach -- from chip core to the cooling tower. In this paper, we propose a data center solution, which has three components: Cooling, Power and Compute. These three components collectively improve efficiency and manageability of the data center by supporting greater compaction, flexible building blocks that can be dynamically configured, dynamic optimization, better monitoring and visualization, and policy-based control. Coefficient of performance (COP) of the ensemble is defined that represents an overall measure of the efficiency of performance of energy flow during the operation of a data center.
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ABSTRACT: The demand for data center solutions with lower total cost of ownership and lower complexity of management is driving the creation of next generation datacenters. The information technology industry is in the midst of a transformation to lower the cost of operation through consolidation and better utilization of critical data center resources. Successful consolidation necessitates increasing utilization of capital intensive “always-on” data center infrastructure, reduction in the recurring cost of power and management of physical resources like water. A 1MW data center operating with water-cooled chillers and cooling towers can consume 18,000 gallons per day to dissipate heat generated by IT equipment. However, this water demand can be mitigated by appropriate use of air-cooled chillers or free cooling strategies that rely on local weather patterns. Water demand can also fluctuate with seasons and vary across geographies. Water efficiency, like energy efficiency is a key metric to evaluate sustainability of the IT ecosystem. In this paper, we propose a procedure for calculation of water efficiency of a datacenter and provide guidance for a management system that can optimize IT performance while managing the tradeoffs between water and energy efficiency in conventional datacenters.Sustainable Systems and Technology, IEEE International Symposium on. 01/2009;
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ABSTRACT: The cost of electricity for datacenters is a substantial operational cost that can and should be managed, not only for saving energy, but also due to the ecologic commitment inherent to power consumption. This work proposes, formalizes and numerically evaluates LEAS, a low-energy scheduling model, for clearing scheduling markets, based on the maximization of welfare, subject to utility-level dependant energy costs. We promote energy-efficient policies in management of datacenters, to enhance the efficiency of modernized datacenters. We focus specifically on linear power models, and the implications of the inherent fixed costs related to energy consumption of modern datacenters. We rigorously test the model by running multiple simulation scenarios derived from real workload traces, and evaluate the results using common statistical methods. We conclude with positive results and implications for long-term sustainable management of modern datacenters.06/2011;
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ABSTRACT: Motivation: Data centers are a critical component of mod- ern IT infrastructure but are also among the worst environ- mental oenders through their increasing energy usage and the resulting large carbon footprints. Ecient management of data centers, including power management, networking, and cooling infrastructure, is hence crucial to sustainabil- ity. In the absence of a \rst-principles" approach to man- age these complex components and their interactions, data- driven approaches have become attractive and tenable. Results: We present a temporal data mining solution to model and optimize performance of data center chillers, a key component of the cooling infrastructure. It helps bridge raw, numeric, time-series information from sensor streams toward higher level characterizations of chiller behavior, suit- able for a data center engineer. To aid in this transduc- tion, temporal data streams are rst encoded into a sym-Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009; 01/2009