Green enterprise computing data: Assumptions and realities

Until now, green computing research has largely relied on few, short-term power measurements to characterize the energy use of enterprise computing. This paper brings new and comprehensive power datasets through Powernet, a hybrid sensor network that monitors the power and utilization of the IT systems in a large academic building. Over more than two years, we have collected power data from 250+ individual computing devices and have monitored a subset of CPU and network loads. This dense, long-term monitoring allows us to extrapolate the data to a detailed breakdown of electricity use across the building´s computing systems. Our datasets provide an opportunity to examine assumptions commonly made in green computing. We show that power variability both between similar devices and over time for a single device can lead to cost or savings estimates that are off by 15-20%. Extending the coverage of measured devices and the duration (to at least one month) significantly reduces errors. Lastly, our experiences with collecting data and the subsequent analysis lead to a better understanding of how one should go about power characterization studies. We provide several methodology guidelines for future green computing research.