Energy Efficiency of Thermal-Aware Job Scheduling Algorithms under Various Cooling Models

One proposed technique to reduce energy consumption of data centers is thermal-aware job scheduling, i.e. job scheduling that relies on predictive thermal models to select among possible job schedules to minimize its energy needs. This paper investigates, using a more realistic linear cooling model, the energy savings of previously proposed thermal-aware job scheduling algorithms, which assume a less realistic model of constant cooling. The results show that the energy savings achieved are greater than the savings previously predicted. The contributions of this paper include: i) linear cooling models should be used in analysis and algorithm design, and ii) although the job scheduler must control the cooling equipment to realize most of the thermal-aware job schedule’s savings, some savings can be still achieved without that control.