Research Progress of Anomaly Detection in IaaS Cloud Operation Driven by Deep Learning
Anomaly detection is an important task in the operation and maintenance of IaaS cloud systems.Through early warning and intervention,serious accidents such as system crashes can be effectively avoided.However,compared to traditional data cen-ters,IaaS cloud systemshave the characteristics of large-scale computing nodes,complex node topology,large monitoring data vo-lume,and lack of data labels,which bring new challenges for IaaS cloud anomaly detection.Starting from the technical framework of deep learning,this paper analyzes the difficulties faced by anomaly detection problems,and summarizes common anomaly detec-tion algorithms and related technologies in IaaS cloud systems.This paper investigates deep learning driven solutions for two typ-ical problems:node anomalies and system anomalies.For node anomalies,detection algorithms driven by temporal data are studied for time-dependent data.For system anomalies,detection algorithms driven by graph data in network topology modeling are inves-tigated.Finally,new issues and challenges in data-driven anomaly detection in IaaS cloud systems are proposed.
Anomaly detectionIaaS cloudTime series dataGraph dataDeep learningMachine learning
NETL
NSTL
万方数据
