Pitt CS 3150 - Power Provisioning for a Warehouse sized Computer (11 pages)

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Power Provisioning for a Warehouse sized Computer



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Power Provisioning for a Warehouse sized Computer

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Pages:
11
School:
University of Pittsburgh
Course:
Cs 3150 - Topics in Algorithms
Topics in Algorithms Documents

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In Proceedings of the ACM International Symposium on Computer Architecture San Diego CA June 2007 Power Provisioning for a Warehouse sized Computer Xiaobo Fan Wolf Dietrich Weber Luiz Andr Barroso Google Inc 1600 Amphitheatre Pkwy Mountain View CA 94043 xiaobo wolf luiz google com ABSTRACT 1 Large scale Internet services require a computing infrastructure that can be appropriately described as a warehouse sized computing system The cost of building datacenter facilities capable of delivering a given power capacity to such a computer can rival the recurring energy consumption costs themselves Therefore there are strong economic incentives to operate facilities as close as possible to maximum capacity so that the non recurring facility costs can be best amortized That is difficult to achieve in practice because of uncertainties in equipment power ratings and because power consumption tends to vary significantly with the actual computing activity Effective power provisioning strategies are needed to determine how much computing equipment can be safely and efficiently hosted within a given power budget In this paper we present the aggregate power usage characteristics of large collections of servers up to 15 thousand for different classes of applications over a period of approximately six months Those observations allow us to evaluate opportunities for maximizing the use of the deployed power capacity of datacenters and assess the risks of over subscribing it We find that even in well tuned applications there is a noticeable gap 7 16 between achieved and theoretical aggregate peak power usage at the cluster level thousands of servers The gap grows to almost 40 in whole datacenters This headroom can be used to deploy additional compute equipment within the same power budget with minimal risk of exceeding it We use our modeling framework to estimate the potential of power management schemes to reduce peak power and energy usage We find that the opportunities for power and



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