[1] CERNY T, DONAHOO M J, TRNKA M. Contextual understanding of microservice archi-
tecture:current and future directions[J].ACM Sigapp Applied Computing Review,2018,17:29-45.
[2] BUSHONG V,ABDELFATTAH A S,MARUF A A, et al. On Microservice Analysis and
Architecture Evolution: A Systematic Mapping Study[J]. Applied Sciences,2021.
[3] ZHOU X,PENG X,XIE T,et al.FaultAnalysis and Debugging of Microservice Systems:
Industrial Survey,Benchmark System,and Empirical Study[J]. IEEE Transactions on Software Engineering,2021,47:243-260.
[4] Hipstershop[EB/OL].https://github.com/abruneauhipstershop.
[5] ZHOU H,CHEN M,LIN Q,et al.Overload Control for Scaling WeChat Microservices[J].
Proceedings of the ACM Symposium on Cloud Computing,2018.
[6] FRANCESCOPD,LAGOP,MALAVOLTAI. Migrating Towards Microservice Architectures:
An Industrial Survey[J].2018 IEEE International Conference on Software Architecture(ICSA), 2018:29-2909.
[7] AIOps[EB/OL].https://www.gartner.com/en/information-technology/glossary/aiops-artificia
l-intelligence-operations.
[8] LI Y,JIANGZM,LIH,et al.PredictingNode Failures in an Ultra-Large-Scale Cloud Com-
puting Platform[J]. ACM Transactions on Software Engineering and Methodology (TOSEM), 2020,29:1-24.
[9] CHANDOLA V,BANERJEE A,KUMARV. Anomaly detection: A survey[J]. ACM Comput.
Surv.,2009,41:15:1-15:58.
[10] HAN S,HUX,HUANG H,et al.ADBench: Anomaly Detection Benchmark[C]//Neural Infor-
mation Processing Systems(NeurIPS).2022.
[11] LIN J,CHEN P,ZHENG Z.Microscope:Pinpoint Performance Issues with Causal Graphs in
Micro-service Environments[C]//ICSOC.2018.
[12] WUL,TORDSSONJ,ELMROTHE,etal. MicroRCA: Root Cause Localization of Perfor-
mance Issues in Microservices[J]. NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium,2020:1-9.
[13] LI Z, CHEN J,JIAO R, et al. Practical Root Cause Localization for Microservice Systems
via Trace Analysis[J]. 2021 IEEE/ACM 29thInternational Symposium on Quality of Service (IWQOS),2021:1-10.
[14] ZHANG C,PENG X,SHA C,etal.DeepTraLog: Trace-Log Combined Microservice Anomaly
Detection through Graph-based Deep Learning[J].2022 IEEE/ACM 44th International Confer-ence on Software Engineering (ICSE),2022:623-634.
[15] CAI Y,HAN B,SU J,et al.TraceModel:An Automatic Anomaly Detection and Root Cause Localization Framework for Microservice Systems[J]. 2021 17th International Conference on Mobility, Sensing and Networking (MSN),2021: 512-519.
[16] LIU P,XU H,OUYANG Q,et al. Unsupervised Detection of Microservice Trace Anomalies through Service-Level Deep Bayesian Networks[J].2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE),2020:48-58.
[17] WU C,ZHAO N,WANG L,et al.Identifying Root-Cause Metrics for Incident Diagnosis in Online Service Systems[J].2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE),2021:91-102.
[18] AGGARWAL C C. Outlier Analysis[CJ/Springer New York.2013.
[19] RUFFL,GORNITZN,DEECKEL,et al. Deep One-Class Classification[C]//ICML. 2018.
[20] KINGMA D P, WELLING M. Auto-Encoding Variational Bayes[J]. CoRR,2014, abs/1312.6114.
[21] LI Z,ZHAO Y,BOTTA N,et al.COPOD: Copula-Based Outlier Detection[J]. 2020 IEEE International Conference on Data Mining(ICDM),2020:118-1123.
[22] LIUF T,TING KM,ZHOUZH.Isolation-Based Anomaly Detection[J]. ACM Trans. Knowl. Discov.Data,2012,6:3:1-3:39.
[23] LATECKILJ,LAZAREVIC A,POKRAJACD. Outlier Detection with Kernel Density Func-tions[C]//MLDM.2007.
[24] ANGIULLI F,PIZZUTI C.Fast OutlierDetection in High Dimensional Spaces[C]/PKDD. 2002.
[25] BREUNIG MM,KRIEGELHP,NGRT,et al.LOF:identifying density-based local outliers [C]//SIGMOD '00.2000.
[26] SHYU M L,CHEN S,SARINNAPAKORN K, et al. A Novel Anomaly Detection Scheme Based on Principal Component Classifier[C]//2003.
[27] AGRAWALP,ABUTARBOUSHHF,GANESH T,et al.Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research(2009-2019)[J].IEEE Access,2021,9:26766-26791.
[28] AIOps Challenge[EB/OL].https://aiops-challenge.com/.
[29] FUDANSELAB.Train ticket[M/OL].GitHub.https://github.com/FudanSELab/train-ticket.
[30] Sock shop[EB/OL].https://github.com/microservices-demo/microservices-demo.
[31] POLI R,KENNEDY J,BLACKWELLTM. Particle swarm optimization[J]. Swarm Intelligence.1995.1:33-57.
[32] GUOZHAOW,WANGL,ZHANGZ.Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm[J]. Neural Computing and Applications,2019,32:9383-9425.
[33] WHITLEYLD.A genetic algorithm tutorial[J]. Statistics and Computing,1994,4:65-85.
[34] COAD[J/OL].GitHub repository.https://github.com/COAD2022/COAD.
[35] GHAHRAMANI Z. Unsupervised learning[M]/BOUSQET O,RAETSCH G,VONLUXBURG U. Lecture Notes in Artificial Intelligence 3176: Advanced lectures on machine learning.Berlin:Springer-Verlag,2004.
[36] MITCHELL TM.Machine learning[M/OL]. McGraw-Hill,2010.http://www.amazon.com/Machine-Learning-Tom-M-Mitchell/dp/0070428077.
[37] XU R, WUNSCH D C. Survey of clustering algorithms[J]. IEEE Transactions on Neural Networks,2005,16:645-678.
[38] SONG L, MA H, WU M, et al. A BriefSurvey of Dimension Reduction[C]/Sino-foreign-interchange Workshop on Intelligent Science and Intelligent Data Engineering. 2018.
[39] CHICCO D,JURMAN G.The advantagesof the Matthews correlation coefficient (MCC) over Fl score and accuracy in binary classification evaluation[J]. BMC Genomics,2020,21.
[40] SHUMWAY R H,STOFFER D S.Time Series Analysis and Its Applications[M]. Springer, 2000.
[41] BRAEIM,WAGNER S.Anomaly Detectionin Univariate Time-series:A Survey on the State-of-the-Art:abs/2004.00433[A].2020.
[42] SHENG WUH.A survey of research on anomaly detection for time series[J].2016 13th International Computer Conference on Wavelet ActiveMedia Technology and Information Processing (ICCWAMTIP),2016:426-431.
[43] KORTE B H, VYGEN J. Combinatorial Optimization: Theory and Algorithms[M/OL]. Springer-Verlag,2012.DOI:10.1007/978-3-642-244889.
[44] CHANDRASHEKAR G,SAHINF.A survey on feature selection methods[J]. Comput. Electr. Eng.,2014,40:16-28.
[45] WANG P,XUJ,MAM,et al.CloudRanger:Root Cause Identification for Cloud Native Systems [J]. 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID),2018:492-502.
[46] XUJ,WANGY,CHENP,et al.Lightweight and Adaptive Service API Performance Monitoring in Highly Dynamic Cloud Environment[J]. 2017 IEEE International Conference on Services Computing (SCC),2017:35-43.
[47] MARIANIL,MONNI C,PEZZE M,et al.Localizing Faults in Cloud Systems[J].2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST),2018:262-273.
[48] Three-sigma rule of thumb[EB/OL].https://en.wikipedia.org/wiki/68-95-99.7_rule.
[49] SHAN H, CHEN Y, LIU H, et al. ?-Diagnosis: Unsupervised and Real-time Diagnosis of Small-window Long-tail Latency in Large-scale Microservice Platforms[J]. The World Wide Web Conference,2019.
[50] ZHANG T,RAMAKRISHNANR,LIVNYM.BIRCH:an efficient data clustering method for very large databases[C]//SIGMOD'96.1996.
[51] JIN M,LV A,ZHU Y,et al. An AnomalyDetection Algorithm for Microservice Architecture Based on Robust Principal Component Analysis[J].IEEE Access,2020,8:226397-226408.
[52] ABDEL-BASSET M,ABDEL-FATAH L,SANGAIAH A K. Metaheuristic Algorithms: A Comprehensive Review[C]//2018.
[53] HOLLAND J H. Adaptation in natural andartificial systems[J]. University of Michigan Press, 1975.
[54] ZHAO Y,NASRULLAH Z,LIZ.PyOD:A Python Toolbox for Scalable Outlier Detection [J/OL]. Journal of Machine Learning Research,2019,20(96):1-7.http://jmlr.org/papers/v20/19-011.html.
[55] COAD[J/OL].GitHub repository.https://github.com/COAD2022/COAD.
[56] THIEUN V,MIRJALILIS.MEALPY:aFramework of The State-of-The-Art Meta-Heuristic Algorithms in Python[CP/OL]. Zenodo,2022. https://doi.org/10.5281/zenodo.6684223.
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