  Estimating power consumption in modern Cloud environments is essential for
carbon quantification toward green computing. Specifically, it is important to
properly account for the power consumed by each of the running applications,
which are packaged as containers. This paper examines multiple challenges
associated with this goal. The first challenge is that multiple customers are
sharing the same hardware platform (multi-tenancy), where information on the
physical servers is mostly obscured. The second challenge is the overhead in
power consumption that the Cloud platform control plane induces. This paper
addresses these challenges and introduces a novel pipeline framework for power
model training. This allows versatile power consumption approximation of
individual containers on the basis of available performance counters and other
metrics. The proposed model utilizes machine learning techniques to predict the
power consumed by the control plane and associated processes, and uses it for
isolating the power consumed by the user containers, from the server power
consumption. To determine how well the prediction results in an isolation, we
introduce a metric termed isolation goodness. Applying the proposed power model
does not require online power measurements, nor does it need information on the
physical servers, configuration, or information on other tenants sharing the
same machine. The results of cross-workload, cross-platform experiments
demonstrated the higher accuracy of the proposed model when predicting power
consumption of unseen containers on unknown platforms, including on virtual
machines.
