SpotLight: Accurate, Explainable and Efficient Anomaly Detection for Open RAN
Chuanhao Sun, Ujjwal Pawar, Molham Khoja, Xenofon Foukas, Mahesh K. Marina, Božidar Radunović · 2024
The Open RAN architecture, with disaggregated and virtualized RAN functions communicating over standardized interfaces, promises a diversified and multi-vendor RAN ecosystem. However, these same features contribute to increased operational complexity, making it highly challenging to troubleshoot RAN related performance issues and failures. Tackling this challenge requires a dependable, explainable anomaly detection method that Open RAN is currently lacking. To address this problem, we introduce SpotLight, a tailored system archtecture with a distributed deep generative modeling based method running across the edge and cloud. SpotLight takes in a diverse, fine grained stream of metrics from the RAN and the platform, to continually detect and localize anomalies. It introduces a novel multi-stage generative model to detect potential anomalies at the edge using a light-weight algorithm, followed by anomaly confirmation and an explain-ability phase at the cloud, that helps identify the minimal set of KPIs that caused the anomaly. We evaluate SpotLight using the metrics collected from an enterprise-scale 5G Open RAN deployment in an indoor office building. Our results show that compared to a range of baseline methods, SpotLight yields significant gains in accuracy (13% higher F1 score), explain-ability (2.3 -- 4X reduction in the number of reported KPIs) and efficiency (4 -- 7X bandwidth reduction).