Anomaly Detection in a Large-scale Cloud Platform

TL;DR

This paper presents an automated deep learning-based anomaly detection system for large-scale cloud platforms, significantly reducing manual monitoring effort and improving reliability.

Contribution

It introduces a scalable, real-time anomaly detection system for cloud platforms using neural networks, with practical implementation insights and best practices.

Findings

Reduces manual monitoring workload for DevOps teams

Detects anomalies in multiple cloud components simultaneously

Enhances customer satisfaction by preventing outages

Abstract

Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. However, this rise in popularity challenges Cloud service providers, as they need to monitor the quality of their ever-growing offerings effectively. To address the challenge, we designed and implemented an automated monitoring system for the IBM Cloud Platform. This monitoring system utilizes deep learning neural networks to detect anomalies in near-real-time in multiple Platform components simultaneously. After running the system for a year, we observed that the proposed solution frees the DevOps team's time and human resources from manually monitoring thousands of Cloud components. Moreover, it increases customer satisfaction by reducing the risk of Cloud outages. In this paper, we share our solutions' architecture, implementation notes, and best practices that emerged while evolving the monitoring system. They can be leveraged by other researchers and practitioners to build anomaly detectors for complex systems.