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.
8 / 2016
Advances in Computer Science Research
AC4DC AC4DC - Adaptive Computing for Green Data Centers Steigerung der Energieeffizienz durch intelligentes Rechenlast- und Infrastrukturmanagement vom Anbieter bis zum Anwender M2DC Modular Microserver DataCentre