Energy efficiency in data center operation depends on many factors, including power distribution, thermal load and consequent cooling costs, and IT management in terms of how and where IT load is placed and moved under changing request loads. Current methods provided by vendors consolidate IT loads onto the smallest number of machines needed to meet application requirements. This paper’s goal is to gain further improvements in energy efficiency by also making such methods ‘spatially aware’, so that load is placed onto machines in ways that respect the efficiency of both cooling and power usage, across and within racks. To help implement spatially aware load placement, we propose a model-based reinforcement learning method to learn and then predict the thermal distribution of different placements for incoming workloads. The method is trained with actual data captured in a fully instrumented data center facility. Experimental results showing notable differences in total power consumption for representative application loads indicate the utility of a two-level spatially-aware workload management (SpAWM) technique in which (i) load is distributed across racks in ways that recognize differences in cooling efficiencies and (ii) within racks, load is distributed so as to take into account cooling effectiveness due to local air flow. The technique is being implemented using online methods that continuously monitor current power and resource usage within and across racks, sense BladeCenter-level inlet temperatures, understand and manage IT load according to an environment’s thermal map. Specifically, at data center level, monitoring informs SpAWM about power usage and thermal distribution across racks. At rack-level, SpAWM workload distribution is based on power caps provided by maximum inlet temperatures determined by CRAC speeds and supply air temperature. SpAWM can be realized as a set of management methods running in VMWare’s ESXServer virtualization infrastructure. Its use has the potential of attaining up to 32% improvements on the CRAC supply temperature requirement compared to non-spatially aware techniques, which can lower the inlet temperature 2∼3°C, that is to say we can increase the CRAC supply temperature 2∼3°C to save nearly 13% −18% cooling energy.
Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center