Data Center Cooling Optimization: Ambient Intelligence Based Load Management (AILM)

Early Data centers can consume 25 to 50 times more electric power than a standard office space of the same footprint. In this paper, a simplified computational fluid dynamics/heat transfer (CFD/HT) model for a unit cell of a data center with a hot aisle-cold aisle (HACA) layout is simulated. Inefficiencies dealing with the mixing of hot air present in the room with the cold inlet air, leading to a loss of cooling potential are identified. The need for a thermal aware job-scheduling algorithm which enhances IT productivity, while maintaining the facility within server inlet temperature constraints is established. The inherent non-linearity of such an optimization problem is explained. A novel algorithm called the Ambient Intelligence based Load Management (AILM) is developed which counters the above issues and enhances the net data center heat dissipation capacity for given energy consumption at the facilities end. It gives a scheme to determine how much and where the computer loads should be allocated, based on the differential loss in cooling potential per unit increase in server workload. Enhancements of heat dissipation capacity of over 50% are proved numerically for the representative values considered. An approach to incorporate heterogeneity in data centers, both for lower heat dissipation and liquid cooled racks has been established. Finally, different objective functions are studied and an ideal combination of the IT objectives and thermal constraints is derived.