  Most recent network failure diagnosis systems focused on data center networks
where complex measurement systems can be deployed to derive routing information
and ensure network coverage in order to achieve accurate and fast fault
localization. In this paper, we target wide-area networks that support
data-intensive distributed applications. We first present a new multi-output
prediction model that directly maps the application level observations to
localize the system component failures. In reality, this application-centric
approach may face the missing data challenge as some input (feature) data to
the inference models may be missing due to incomplete or lost measurements in
wide area networks. We show that the presented prediction model naturally
allows the {\it multivariate} imputation to recover the missing data. We
evaluate multiple imputation algorithms and show that the prediction
performance can be improved significantly in a large-scale network. As far as
we know, this is the first study on the missing data issue and applying
imputation techniques in network failure localization.
