Early Detection of Signal and Process Anomalies in Enterprise Computing Systems.

Abstract

This paper presents a real-time machine learning technique that has been adapted from the field of statistical process control (SPC) to give early annunciation of incipient anomalies in signals and processes involving enterprise computing systems and associated networks. A binary-hypothesis technique called the sequential probability ratio test (SPRT) provides optimal detection of change points for online surveillance of digitized signals and demonstrates the dual advantages of high sensitivity with good avoidance of false alarms. SPRT-based systems are being developed for a variety of applications to enhance the reliability, availability, and serviceability of enterprise computing systems.