Anomaly Detection in a Large-scale Cloud Platform
This addresses the challenge for cloud service providers in managing growing workloads, though it is incremental as it applies existing deep learning methods to a new domain.
The paper tackles the problem of monitoring large-scale cloud platforms by designing an automated system using deep learning to detect anomalies in near-real-time across multiple components, which freed DevOps resources and increased customer satisfaction by reducing outage risks.
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.