Visualization and Analysis of System Monitoring Data using Multi-resolution Context Information

Projection of high dimensional data into a lower dimensional subspace is required for human understanding of the health of an IT infrastructure. Over the past several years, there have been a large number of dimensionality reduction techniques that have been proposed. Their direct application for visualizing system monitoring data is challenged by two factors. First, system monitoring data does not lie in a metric space. Second, system monitoring data is intrinsically iquestmulti-resolutioniquest in that an event may lead to cascaded events (a server going down impacts one or more applications running on the server; a network outage may impact several network dependent components). Lower dimensional representations which do not take into account the intrinsic multi-resolution nature of the monitoring data are thus limited in their utility and challenge human comprehension. In this paper, we exploit the multi-resolution nature of the monitoring data and a 1-of-n representation of event data to construct navigable multi-resolution topology preserving views for visualizing system monitoring data. We also demonstrate the efficacy of the proposed approach using data from a real data center.