[irq]: techie interrupted

» UBL: Unsupervised Behavior Learning for Predicting Performance Anomalies in Virtualized Cloud Systems [pdf]

“Infrastructure-as-a-Service (IaaS) clouds are prone to performance
anomalies due to their complex nature. Although previous work
has shown the effectiveness of using statistical learning to detect
performance anomalies, existing schemes often assume labelled
training data, which requires significant human effort and can only
handle previously known anomalies. We present an Unsupervised
Behavior Learning (UBL) system for IaaS cloud computing infras-
tructures. UBL leverages Self-Organizing Maps to capture emer-
gent system behaviors and predict unknown anomalies. For scala-
bility, UBL uses residual resources in the cloud infrastructure for
behavior learning and anomaly prediction with little add-on cost.
We have implemented a prototype of the UBL system on top of the
Xen platform and conducted extensive experiments using a range
of distributed systems. Our results show that UBL can predict per-
formance anomalies with high accuracy and achieve sufficient lead
time for automatic anomaly prevention. UBL supports large-scale
infrastructure-wide behavior learning with negligible overhead.”