Systems Seminar Talk: August 31, 2012

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 infrastructures. UBL leverages Self-Organizing Maps to capture emergent system behaviors and predict unknown anomalies. For scalability, 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 performance anomalies with high accuracy and achieve sufficient lead time for automatic anomaly prevention. UBL supports large-scale infrastructure-wide behavior learning with negligible overhead.