  While monitoring system behavior to detect anomalies and failures is
important, existing methods based on log-analysis can only be as good as the
information contained in the logs, and other approaches that look at the
OS-level software state introduce high overheads. We tackle the problem with
syslrn, a system that first builds an understanding of a target system offline,
and then tailors the online monitoring instrumentation based on the learned
identifiers of normal behavior. While our syslrn prototype is still preliminary
and lacks many features, we show in a case study for the monitoring of
OpenStack failures that it can outperform state-of-the-art log-analysis systems
with little overhead.

Machine Learning;Distributed, Parallel, and Cluster Computing;Operating Systems