  Most IT systems depend on a set of configuration variables (CVs), expressed
as a name/value pair that collectively define the resource allocation for the
system. While the ill-effects of misconfiguration or improper resource
allocation are well-known, there is no effective a priori metric to quantify
the impact of the configuration on the desired system attributes such as
performance, availability, etc. In this paper, we propose a
\textit{Configuration Health Index} (CHI) framework specifically attuned to the
performance attribute to capture the influence of CVs on the performance
aspects of the system. We show how CHI, which is defined as a configuration
scoring system, can take advantage of the domain knowledge and the available
(but rather limited) performance data to produce important insights into the
configuration settings. We compare the CHI with both well-advertised segmented
non-linear models and state-of-the-art data-driven models, and show that the
CHI not only consistently provides better results but also avoids the dangers
of pure data drive approach which may predict incorrect behavior or eliminate
some essential configuration variables from consideration.
