GreenSensing: A Fine Grained Power Monitoring System for a Network of Computers

Recent studies have shown the necessity of fine grained power usage visibility to encourage user behavior energy conservation. Existing works toward this direction are mainly focused on residential monitoring scenarios. This study considers another important scenario of providing fine grained power usage information over a set of networked computers. Such scenario could be applied to a wide range of workspaces, including research labs in colleges and business offices in companies. To our best knowledge, no existing systems provide cost-effective solutions for such scenario. To this end, we present GreenSensing which provides real time individual power usage estimation by leveraging the fact that the amount of power usage is highly correlated to CPU usage rate of a computer. As CPU usage can be obtained by a software program, we can have the power information without real metering instruments, and therefore have the benefit of zero infrastructure cost. However, due to the indirectness, before the estimation works, a proper calibration for computers is required, which needs lots of human intervention. To this problem, we propose a novel framework that empowers GreenSensing to automatically and simultaneously calibrate multiple computers on the fly. We show through experiments GreenSensing provides only 4.1% to 6.2% estimation error on individual power usage.