Power data alone cannot identify sources of energy inefficiency. However, correlating power data with utilization statistics can reveal where power is used well and where it is wasted. We describe a sensing in- frastructure, PowerNet, that monitors power and utilization in a building environment. The deployment includes both wired and wireless sensors and cov- ers offices, networking closets, and server racks. We present PowerNets architecture, then generate ini- tial insights from each monitored environment. Ana- lyzing PowerNet data traces identifies contexts where electricity consumption can be reduced without cost, and others which call for rethinking system designs altogether.
CITATION STYLE
Kazandjieva, M. A., Heller, B., Levis, P., & Kozyrakis, C. (2009). Energy Dumpster Diving. In Proceedings of the Second Workshop on Power Aware Computing (HotPower’09). San Diego: ACM. Retrieved from http://sing.stanford.edu/pubs/dumpster.pdf
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