Adaptive Anomaly Detection in Performance Metric Streams

Continuous detection of performance anomalies such as service degradations has become critical in cloud and Internet services due to impact on quality of service and end-user experience. However, the volume and fast changing behavior of metric streams have rendered it a challenging task. Many diagnosis frameworks often rely on thresholding with stationarity or normality assumption, or on complex models requiring extensive offline training. Such techniques are known to be prone to spurious false-alarms in online settings as metric streams undergo rapid contextual changes from known baselines. Hence, we propose two unsupervised incremental techniques following a two-step strategy. First, we estimate an underlying temporal property of the stream via adaptive learning and, then we apply statistically robust control charts to recognize deviations. We evaluated our techniques by replaying over 40 time-series streams from the Yahoo! Webscope S5 datasets as well as four other traces of real Web service QoS and ISP traffic measurements. Our methods achieve high detection accuracy and few false-alarms, and better performance in general compared to an open-source package for time-series anomaly detection.