  Sustainable software engineering has received a lot of attention in recent
times, as we witness an ever-growing slice of energy use, for example, at data
centers, as software systems utilize the underlying infrastructure.
Characterizing servers for their energy use accurately without being intrusive,
is therefore important to make sustainable software deployment choices. In this
paper, we introduce ESAVE which is a machine learning-based approach that
leverages a small set of hardware attributes to characterize a server or
virtual machine's energy usage across different levels of utilization. This is
based upon an extensive exploration of multiple ML approaches, with a focus on
a minimal set of required attributes, while showcasing good accuracy. Early
validations show that ESAVE has only around 12% average prediction error,
despite being non-intrusive.
