Networking

Toward Automated Anomaly Identification in Large-Scale Systems

Download Now Free registration required

Executive Summary

When a system fails to function properly, health-related data are collected for troubleshooting. However, it is challenging to effectively identify anomalies from the voluminous amount of noisy, high-dimensional data. The traditional manual approach is time-consuming, error-prone, and even worse, not scalable. In this paper, the authors present an automated mechanism for node-level anomaly identification in large-scale systems. A set of techniques are presented to automatically analyze collected data: data transformation to construct a uniform data format for data analysis, feature extraction to reduce data size, and unsupervised learning to detect the nodes acting differently from others.

  • Format: PDF
  • Size: 1587.4 KB