Green Enterprise Computing Data: Assumptions and Realities
Published in Proceedings of the Third International Green Computing Conference (IGCC), June 2012.
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.
Data (WWW), Talk (6MB), Paper (339KB)
BibTeX entry
@inproceedings{igcc12kazandjieva, author = "Maria Kazandjieva and Brandon Heller and Omprakash Gnawali and Philip Levis and Christos Kozyrakis", title = "{Green Enterprise Computing Data: Assumptions and Realities}", booktitle = "{Proceedings of the Third International Green Computing Conference (IGCC)}", year = {2012}, month = {June} }