Cisco Systems, Inc.
Data-driven identification and selection of features related to a state change of a network component

Abstract:

Techniques and mechanisms for automatically identifying counters/features of a network component that are related to a state change (or event) for the network component or for the network itself. For example, using data obtained from the network component around a time of the state change, delta-averages for the counters/features around the time of the state change may be determined. The delta-averages may be utilized to determine which counters/features are most descriptive for a particular state change. Determining which counters/features are most descriptive may also include determining which counters/features are most relevant, i.e., counters/features that contribute most to preserving the manifold structure of the original data or counters/features with the highest or lowest correlation with the other counters/features in the data set. Thus, the techniques described herein provide for an approach to distill which counters/features contribute the most to a particular state change from a data driven perspective.