MFGAD-INT: in-band network telemetry data-driven anomaly detection using multi-feature fusion graph deep learning

Abstract

Abstract As the cloud services market grows, cloud management tools that detect network anomalies in a non-intrusive manner are critical to improve users’ experience of cloud services. However, some network anomalies, such as Microburst, in cloud systems are very discreet. Network monitoring methods, e.g., SNMP, Ping, are of coarse temporal granularity or low-dimension metrics, have difficulty to identify such anomalies quickly and accurately. Network telemetry is able to collect rich network metrics with fine temporal granularity, which can provide deep insight into network anomalies. However, the rich features in the telemetry data are insufficient exploited in existing research. This paper proposes a Multi-feature Fusion Graph Deep learning approach driven by the In-band Network Telemetry, shorted as MFGAD-INT, to efficiently extract and process the spatial-temporal correlation information in telemetry data and effectively identify the anomalies. The experimental results show that the accuracy performance of the proposed method improves about 10.56% compared to the anomaly detection method without network telemetry and about 9.73% compared to the network telemetry-based method.

Keywords

• Anomaly detection
• Time series analysis
• In-band network telemetry
• Deep learning
• Data stream mining
• Cloud computing