Performance Anomaly and Change Point Detection For Large-Scale System Management

Abstract

We begin by presenting a short overview of the classical Statistical Process Control based Anomaly Detection techniques and tools including Multivariate Adaptive Statistical Filtering, Statistical Exception Detection System, Exception Value meta-metric based Change Point Detection, control chart, business driven massive prediction and methods of using them to manage large-scale systems (with real examples of applying that to large financial companies) such as on-prem servers fleet, or massive clouds. Then we will turn to the presentation of modern techniques of anomaly and normality detection, such as deep learning and entropy-based anomalous pattern detections (also successfully tested against a large amount of real performance data of a large bank).