DESTIN: Detecting State Transitions in Network elements

Operators are interested in gaining a comprehensive assessment of their network elements and tracking operational changes. Commonly, this assessment is achieved by performing regular checks of different operational counters and defining expert rules from known root causes. The common approach requires the maintenance of a regularly updated set of rules and only goes as far as the operator's pre-gained knowledge of the system. In this paper, a broader set of counters (not limited to the handpicked Key Performance Indicators (KPIs)) is explored with an unsupervised approach. The goal is to leverage the dependencies between the counters in order to discover complex state changes that might have otherwise slipped the operator's view. This paper proposes DESTIN, a multivariate unsupervised change detection for high dimensional time-series data of originally low effective dimension, which provides near real-time state assessment of network device. The efficiency of the method is demonstrated on an experimental test-bed.