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Lifetime Exergy Consumption as a Sustainability Metric for Enterprise Servers

[+] Author Affiliations
Christopher R. Hannemann, Van P. Carey

University of California-Berkeley, Berkeley, CA

Amip J. Shah, Chandrakant Patel

Hewlett Packard Laboratories, Palo Alto, CA

Paper No. ES2008-54181, pp. 35-42; 8 pages
doi:10.1115/ES2008-54181
From:
  • ASME 2008 2nd International Conference on Energy Sustainability collocated with the Heat Transfer, Fluids Engineering, and 3rd Energy Nanotechnology Conferences
  • ASME 2008 2nd International Conference on Energy Sustainability, Volume 1
  • Jacksonville, Florida, USA, August 10–14, 2008
  • Conference Sponsors: Advanced Energy Systems Division and Solar Energy Division
  • ISBN: 978-0-7918-4319-2 | eISBN: 0-7918-3832-3
  • Copyright © 2008 by ASME

abstract

As the use of information technology becomes more ubiquitous, the need for data processing and storage capabilities increases. This results in the construction and operation of large data centers—facilities that house thousands of servers and serve as the backbone for all types of computational processes. Unfortunately, as processing power and storage capacity increases, so does the corresponding power and cooling requirements of the data centers. Several studies have examined the efficiency of data centers by focusing on server and cooling power inputs, but this fails to capture the data center’s entire impact. To accomplish this, the use of a lifetime exergy (available energy) analysis is proposed. This study first details the development of a lifetime exergy consumption model designed specifically for data center analysis. To create a database of computer components, a disassembly analysis was performed, and the results are detailed. By combining the disassembly analysis of a server with the aggregation of energy and material data, a more rigorous and useful assessment of the server’s overall impact is demonstrated. The operation of the lifetime exergy consumption model is demonstrated by case studies examining the effects of variance in transportation and cooling strategies. The importance of transportation modes and material mass, which are greatly affected by supply chain parameters, is shown. The impact of static and dynamic cooling within data centers is also demonstrated.

Copyright © 2008 by ASME

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