Self-Learning Health-Status Analysis for a Core Router System

Abstract

The health status of core router systems needs to be analyzed efficiently in order to ensure high reliability and timely error recovery. Although a large amount operational data is collected from core routers, only a small part of this data is labeled by experts. The lack of labels is an impediment towards the adoption of supervised learning. We present an iterative self-learning procedure for assessing the health status of a core router. This procedure first computes a representative feature matrix to capture different characteristics of time-series data. Hierarchical clustering is then utilized to infer labels for the unlabeled dataset. Finally, a classifier is built and iteratively updated using both labeled and unlabeled dataset. Field data collected from a set of commercial core routers are used to experimentally validate the proposed health-status analyzer.