Exploring Hardware Profile-Guided Green Datacenter Scheduling

Abstract

Recently, tapping into renewable energy sources has shown great promise in alleviating server energy poverty and reducing IT carbon footprint. Due to the limited, time-varying green power generation, matching server power demand to runtime power budget is often crucial in green data centers. However, existing studies mainly focus on the temporal variability of the power supply and demand, while largely ignore the spatial variation issue in server hardware. With more complex computing units integrated and the technology scaling, the performance/power variation among nodes and the conservative supply voltage margin of each core can greatly compromise the power matching effectiveness that a green datacenter can achieve. This paper explores green datacenter design that takes into account non-uniform hardware power characteristics. We propose is cope, a novel power management framework that can (1) expose architecture variability to the datacenter facility-level scheduler for efficient power matching, and (2) balance the energy usage and lifetime of compute nodes in the highly dynamic green computing environment. Using realistic hardware profiling data and renewable energy data, we show that is cope can reduce the energy cost up to 54%, while maintaining fairly balanced processor utilization rate and negligible profiling overhead.