research-article

On building next generation data centers: energy flow in the information technology stack

Authors:

Mohandas Mekanapurath,

Sankaragopal Velayudhan,

Manu Kumar, VAuthors Info & Claims

COMPUTE '08: Proceedings of the 1st Bangalore Annual Compute Conference

Article No.: 8, Pages 1 - 7

https://doi.org/10.1145/1341771.1341780

Published: 18 January 2008 Publication History

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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, 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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Callou GFerreira JMaciel PTutsch DSouza R(2014)An Integrated Modeling Approach to Evaluate and Optimize Data Center Sustainability, Dependability and CostEnergies10.3390/en70102387:1(238-277)Online publication date: 8-Jan-2014
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On building next generation data centers: energy flow in the information technology stack

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Published In

COMPUTE '08: Proceedings of the 1st Bangalore Annual Compute Conference

January 2008

195 pages

ISBN:9781595939500

DOI:10.1145/1341771

Program Chair:
R. K. Shyamasundar
IBM Research Labs

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 18 January 2008

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COMPUTE08: ACM Bangalore Chapter COMPUTE 2008

January 18 - 20, 2008

Bangalore, India

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Overall Acceptance Rate 114 of 622 submissions, 18%

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Stillmaker ABohnenstiehl BStillmaker LBaas B(2020)Scalable energy-efficient parallel sorting on a fine-grained many-core processor arrayJournal of Parallel and Distributed Computing10.1016/j.jpdc.2019.12.011138:C(32-47)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.jpdc.2019.12.011
Callou GFerreira JMaciel PTutsch DSouza R(2014)An Integrated Modeling Approach to Evaluate and Optimize Data Center Sustainability, Dependability and CostEnergies10.3390/en70102387:1(238-277)Online publication date: 8-Jan-2014
https://doi.org/10.3390/en7010238
Menarini MSeracini FZhang XRosing TKrüger ILago PMeyer NMorisio MMüller HScanniello G(2013)Green web servicesProceedings of the 2nd International Workshop on Green and Sustainable Software10.5555/2662693.2662696(8-15)Online publication date: 20-May-2013
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Menarini MSeracini FZhang XRosing TKruger I(2013)Green web services: Improving energy efficiency in data centers via workload predictions2013 2nd International Workshop on Green and Sustainable Software (GREENS)10.1109/GREENS.2013.6606416(8-15)Online publication date: May-2013
https://doi.org/10.1109/GREENS.2013.6606416
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Bodenstein CHedwig MNeumann DCaton SNeumann DRana OSakellariou RWeinhardt C(2011)Low-energy automated scheduling of computing resourcesProceedings of the 1st ACM/IEEE workshop on Autonomic computing in economics10.1145/1998561.1998566(11-18)Online publication date: 14-Jun-2011
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Patnaik DMarwah MSharma RRamakrishnan N(2011)Temporal data mining approaches for sustainable chiller management in data centersACM Transactions on Intelligent Systems and Technology10.1145/1989734.19897382:4(1-29)Online publication date: 15-Jul-2011
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