  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 bottomline). Exogenic and/or endogenic factors often give rise to breakouts
in cloud data which makes maintaining high availability and delivering high
performance very challenging.
  Although there exists a large body of prior research in breakout detection,
existing techniques are not suitable for detecting breakouts in cloud data
owing to being not robust in the presence of anomalies.
  To this end, we developed a novel statistical technique to automatically
detect breakouts in cloud data. In particular, the technique employs Energy
Statistics to detect breakouts in both application as well as system metrics.
Further, the technique uses robust statistical metrics, viz., median, 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 times faster than a state-of-the-art technique for breakout detection and
is being currently used on a daily basis at Twitter.
