@inproceedings{Dan2016,
Author = {Daniel Schlitt, Wolfgang Nebel},
Title = {Data Center Performance Model for Evaluating Load Dependent Energy Efficiency},

Year = {2016},
Pages = {42-51},
Month = {8},
Editor = {Paola Grosso, Patricia Lago, Anwar Osseyran},
Publisher = {Atlantis Press},
Series = {Advances in Computer Science Research},
Edition = {46},
Booktitle = {Proceedings of ICT for Sustainability 2016},
type = {inproceedings},
note = {Energy efficiency metrics are important tools for data center operators to optimize their facilities and thereby decreasing operational expenses while strengthen competitiveness. However, commonly used metrics like Power Usage Effectiveness do not conside},
Abstract = {Energy efficiency metrics are important tools for data center operators to optimize their facilities and thereby decreasing operational expenses while strengthen competitiveness. However, commonly used metrics like Power Usage Effectiveness do not consider productivity or suitable proxy indicators, thus lacking the ability for correctly comparing energy efficiency between data centers. Also, other known metrics which consider productivity, do this in a subjective way, i.e. results are only comparable for the same definitions. In order to address these shortcomings we proposed the Load Dependent Energy Efficiency (LDEE) metric, which uses a combination of utilization, performance, and power models to provide detailed efficiency data. By using load dependent models the concrete workloads are abstracted realizing comparability. Furthermore, models are trained with public information such as hardware specifications and benchmark results to avoid disruption of operation and thereby increasing applicability. This paper focuses on the utilization and performance models of LDEE.}
}
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