Anomaly Detection and Failure Root Cause Analysis in (Micro) Service-Based Cloud Applications: A Survey

被引:68
作者
Soldani, Jacopo [1 ]
Brogi, Antonio [1 ]
机构
[1] Univ Pisa, Dipartimento Informat, Largo B Pontecorvo 3, I-56127 Pisa, PI, Italy
关键词
Microservices; multi-service applications; failure detection; anomaly detection; root cause analysis; PERFORMANCE DIAGNOSIS; FAULT LOCALIZATION; MICROSERVICES;
D O I
10.1145/3501297
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The proliferation of services and service interactions within microservices and cloud-native applications, makes it harder to detect failures and to identify their possible root causes, which is, on the other hand crucial to promptly recover and fix applications. Various techniques have been proposed to promptly detect failures based on their symptoms, viz., observing anomalous behaviour in one or more application services, as well as to analyse logs or monitored performance of such services to determine the possible root causes for observed anomalies. The objective of this survey is to provide a structured overview and qualitative analysis of currently available techniques for anomaly detection and root cause analysis in modern multi-service applications. Some open challenges and research directions stemming out from the analysis are also discussed.
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页数:39
相关论文
共 98 条
  • [1] Abdi H., 2010, ENCY RES DESIGN, V1, P1, DOI DOI 10.4135/9781412961288.N178
  • [2] Localization of Operational Faults in Cloud Applications by Mining Causal Dependencies in Logs Using Golden Signals
    Aggarwal, Pooja
    Gupta, Ajay
    Mohapatra, Prateeti
    Nagar, Seema
    Mandal, Atri
    Wang, Qing
    Paradkar, Amit
    [J]. SERVICE-ORIENTED COMPUTING, ICSOC 2020, 2021, 12632 : 137 - 149
  • [3] Graph based anomaly detection and description: a survey
    Akoglu, Leman
    Tong, Hanghang
    Koutra, Danai
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2015, 29 (03) : 626 - 688
  • [4] AN INTRODUCTION TO KERNEL AND NEAREST-NEIGHBOR NONPARAMETRIC REGRESSION
    ALTMAN, NS
    [J]. AMERICAN STATISTICIAN, 1992, 46 (03) : 175 - 185
  • [5] [Anonymous], 2014, Microservices
  • [6] [Anonymous], 2020, ISTIO
  • [7] [Anonymous], 2021, PROM MON SYST TIM SE
  • [8] Arnold A, 2007, KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P66
  • [9] Evaluation of Causal Inference Techniques for AIOps
    Arya, V
    Shanmugam, K.
    Aggarwal, P.
    Wang, Q.
    Mohapatra, P.
    Nagar, S.
    [J]. CODS-COMAD 2021: PROCEEDINGS OF THE 3RD ACM INDIA JOINT INTERNATIONAL CONFERENCE ON DATA SCIENCE & MANAGEMENT OF DATA (8TH ACM IKDD CODS & 26TH COMAD), 2021, : 188 - 192
  • [10] Basseville M., 1993, DETECTION ABRUPT CHA