Abstract
The management of large-scale distributed information
systems relies on the effective use and modeling of monitoring
data collected at various points in the distributed information
systems. A promising approach is to discover invariant relationships
among the monitoring data and generate invariant
networks, where a node is a monitoring data source (metric) and
a link indicates an invariant relationship between two monitoring
data. Such an invariant network representation can help system
experts to localize and diagnose the system faults by examining
those broken invariant relationships and their related metrics,
because system faults usually propagate among the monitoring
data and eventually lead to some broken invariant relationships.
However, at one time, there are usually a lot of broken links
(invariant relationships) within an invariant network. Without
proper guidance, it is difficult for system experts to manually
inspect this large number of broken links. Thus, a critical
challenge is how to effectively and efficiently rank metrics (nodes)
of invariant networks according to the anomaly levels of metrics.
The ranked list of metrics will provide system experts with useful
guidance for them to localize and diagnose the system faults. To
this end, we propose to model the nodes and the broken links as a
Markov Random Field (MRF), and develop an iteration algorithm
to infer the anomaly of each node based on belief propagation
(BP). Finally, we validate the proposed algorithm on both realworld
and synthetic data sets to illustrate its effectiveness.
