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.
- SourceAvailable from: Chandrakant D Patel
- "We recently consolidated 14 laboratory data centers into one large site in Bangalore, India, and applied a dynamic control system that adjusts the utilization of the air conditioning system based on 7,500 temperature sensors deployed throughout the data center. The 40 per cent savings in cooling power consumption achieved in this facility translates to annual savings of approximately $1.2 million, when compared to the conventional approach . "
Conference Paper: Sustainable Data Centers: Enabled by Supply and Demand Side Management[Show abstract] [Hide abstract]
ABSTRACT: The environmental impact of data centers is significant and is growing rapidly. Servers alone in the US consumed 1.2% of the nation's energy in 2005, according to the EPA. In the following year, the EPA found that the cost of energy rose by 10%. However, there are many opportunities for greater efficiency through integrated design and management of data center components. To that end, we propose a sustainable data center that replaces conventional resource delivery models with a framework centered around the supply and demand side management of all data center resources including IT, power and cooling. We have identified five elements for achieving this vision: data center scale lifecycle design, flexible and configurable building blocks, pervasive cross-layer sensing, knowledge discovery and visualization, and autonomous control. We describe these principles and provide selected results that quantify the potential for savings.Proceedings of the 46th Design Automation Conference, DAC 2009, San Francisco, CA, USA, July 26-31, 2009; 01/2009
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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
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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.