Universal fault detection for NFV using SOM-based clustering

Abstract

Network function virtualization (NFV) introduces additional complexity to network management, since the placement and behavior of virtualized network functions (VNFs) can be independent from the underlying hardware, and virtualization technology increases the number of monitoring points and the amount of statistical data. In our previous work, we proposed a framework for detecting anomalous behavior of VNFs using a SOM-based technique. The solution relies upon manually configuring the SOM clustering parameters and selecting the statistics for each failure type in advance, which results in a high maintenance load. In this paper, we provide a solution that is universal in the sense that a range of different faults can be detected using a single set of local statistics and SOM clustering parameters. Experimental results from a testbed show that faults, including memory leak, packet congestion, and session congestion, can be detected with high accuracy using only four types of performance statistics.