Symbol-based health-status analysis in a core router system

Abstract

To ensure high reliability and rapid error recovery in commercial core router systems, a health-status analyzer is essential to monitor the different features of core routers. However, traditional health analyzers need to store a large amount of historical data in order to identify health status. The storage requirement becomes prohibitively high when we attempt to carry out long-term health-status analysis for a large number of core routers. We describe the design of a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence to do health analysis. The symbolic aggregation approximation (SAX) and moving-average-based trend approximation methods are implemented to encode complex time series in a hierarchical way. Hierarchical agglomerative clustering and sequitur rule discovery are implemented to learn important global and local patterns. Two classification methods are then utilized to identify the health status of core routers. Data collected from a set of commercial core router systems are used to validate the proposed health-status analyzer. The experimental results show that our symbol-based health status analyzer requires much lower storage than traditional methods, but can still maintain comparable diagnosis accuracy.