Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230400016-8.doi: 10.11896/jsjkx.230400016

• Network & Communication • Previous Articles     Next Articles

Research Progress of Anomaly Detection in IaaS Cloud Operation Driven by Deep Learning

SI Jia1, LIANG Jianfeng1, XIE Shuo1, DENG Yingjun2   

  1. 1 National Marine Data and Information Service,Tianjin 300171,China
    2 Center for Applied Mathematics,Tianjin University,Tianjin 300072,China
  • Published:2024-06-06
  • About author:SI Jia,born in 1994,postgraduate,assistant engineer.Her main research interests include marine information system and so on.
    DENG Yingjun,born in 1986,Ph.D,lecturer.His main research interests include predictive maintenance and machine learning.
  • Supported by:
    National Marine Data and Information Service Youth Fund Project(202102006) and Open Fund of State Key Laboratory of Marine Resources Utilization in South China Sea(MRUKF2021035).

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Abstract: 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 centers,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 detection algorithms and related technologies in IaaS cloud systems.This paper investigates deep learning driven solutions for two typical 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 investigated.Finally,new issues and challenges in data-driven anomaly detection in IaaS cloud systems are proposed.

Key words: Anomaly detection, IaaS cloud, Time series data, Graph data, Deep learning, Machine learning

CLC Number: 

  • TP311.1
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