Ranking Metric Anomaly in Invariant Networks

摘要

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 traditional approach to model monitoring data is to discover invariant relationships among the monitoring data. Indeed, we can discover all invariant relationships among all pairs of 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, since 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. To this end, in this article, we propose the problem of ranking metrics according to the anomaly levels for a given invariant network, while this is a nontrivial task due to the uncertainties and the complex nature of invariant networks. Specifically, we propose two types of algorithms for ranking metric anomaly by link analysis in invariant networks. Along this line, we first define two measurements to quantify the anomaly level of each metric, and introduce the mRank algorithm. Also, we provide a weighted score mechanism and develop the gRank algorithm, which involves an iterative process to obtain a score to measure the anomaly levels. In addition, some extended algorithms based on mRank and gRank algorithms are developed by taking into account the probability of being broken as well as noisy links. Finally, we validate all the proposed algorithms on a large number of real-world and synthetic data sets to illustrate the effectiveness and efficiency of different algorithms.