Leveraging cloud data to mitigate user experience from ‘breaking bad’

Low latency and high availability of an app or a web service are key, amongst other factors, to the overall user experience (which in turn directly impacts the bottoniline). Exogenic and/or endogenic factors often give rise to breakouts in cloud data which makes maintaining high availability and delivering high performance very challenging. Existing breakout detection techniques are not suitable for cloud data owing to not being robust in the presence of anomalies. To this end, we developed a novel statistical technique to automatically detect breakouts in cloud data. This technique employs Energy Statistics to detect breakouts in both app and system metrics. Further, the technique uses robust statistical metrics, viz., medians, and estimates the statistical significance of a breakout through a permutation test. To the best of our knowledge, this is the first work which addresses breakout detection in the presence of anomalies. We demonstrate the efficacy of the proposed technique using production data and report precision, recall, and f-measure measure. The proposed technique is 3.5× faster than a state-of-the-art technique for breakout detection and is being currently used on a daily basis at Twitter Inc.