  As contemporary software-intensive systems reach increasingly large scale, it
is imperative that failure detection schemes be developed to help prevent
costly system downtimes. A promising direction towards the construction of such
schemes is the exploitation of easily available measurements of system
performance characteristics such as average number of processed requests and
queue size per unit of time. In this work, we investigate a holistic
methodology for detection of abrupt changes in time series data in the presence
of quasi-seasonal trends and long-range dependence with a focus on failure
detection in computer systems. We propose a trend estimation method enjoying
optimality properties in the presence of long-range dependent noise to estimate
what is considered "normal" system behaviour. To detect change-points and
anomalies, we develop an approach based on the ensembles of "weak" detectors.
We demonstrate the performance of the proposed change-point detection scheme
using an artificial dataset, the publicly available Abilene dataset as well as
the proprietary geoinformation system dataset.
