Abstract
The energy required to cool an air-cooled data center (DC) contributes significantly to the cost of operation, which is further exacerbated due to a poor choice of cooling architecture and ineffective IT workload management. Although existing algorithms reduce energy consumption, they do not minimize thermodynamic irreversibility by design. We provide a tradeoff approach that simultaneously minimizes power usage effectiveness PUE and maximizes the exergy efficiency η2nd. The temperature field is predicted inside a contained single-rack DC that is equipped with a rack-mountable cooling unit (RMCU) based on a mechanical resistance model for the fluid flow. This thermal model informs a multi-objective optimization framework based on a genetic algorithm to determine the optimal decision variables and tradeoffs for PUE and η2nd. We investigate the interrelated effects of (1) guidelines that ensure the reliability of the IT equipment, (2) overall network traffic load, (3) spatial IT load distribution, (4) changes in cooling system variables, and (5) multi-objective optimization. Results for the single rack system are presented in a scalable dimensionless form that is applicable for a multi-rack DC containing RMCUs. By considering the first and second laws of thermodynamics, this novel approach improves workload scheduling from both energy and exergy perspectives.
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