A simple approach to detect anomalies in microservices-based systems using PyOD

Detalhes bibliográficos
Autor(a) principal: Landim, Lauriana Patricia Tavares
Data de Publicação: 2022
Outros Autores: Barata, Luís, Lopes, Eurico
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.11/8472
Resumo: Ease of scale is one of the defining characteristics of microservices. However, with scalability comes the problem of diversity of services, making it very important to detect anomalies the soonest possible. Because it is recent, there are still few studies on the best approaches to detecting anomalies in microservices. This paper proposes the Python toolkit, PyOD, as an approach for microservice anomaly detection. This toolkit is composed of a set of anomaly detection algorithms, including classical LOF (SIGMOD2000) to the latest ECOD (TKDE2022). To evaluate the approach, we used two of its algorithms, k Nearest Neighbors (kNN) and Histogram-based Outlier Score (HBOS) to detect anomalies such as application bugs, CPU exhausted, and network jam on the TraceRCA dataset. This dataset contains logs from a real microservices system. The preliminary results show that HBOS algorithm performs better than kNN, with Recall and F1-Score of 93% and 89%, respectively, while for kNN these metrics were 92% and 85%, respectively.
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spelling A simple approach to detect anomalies in microservices-based systems using PyODAnomaly detectionPyODOutliers algorithmsMicroservicesEase of scale is one of the defining characteristics of microservices. However, with scalability comes the problem of diversity of services, making it very important to detect anomalies the soonest possible. Because it is recent, there are still few studies on the best approaches to detecting anomalies in microservices. This paper proposes the Python toolkit, PyOD, as an approach for microservice anomaly detection. This toolkit is composed of a set of anomaly detection algorithms, including classical LOF (SIGMOD2000) to the latest ECOD (TKDE2022). To evaluate the approach, we used two of its algorithms, k Nearest Neighbors (kNN) and Histogram-based Outlier Score (HBOS) to detect anomalies such as application bugs, CPU exhausted, and network jam on the TraceRCA dataset. This dataset contains logs from a real microservices system. The preliminary results show that HBOS algorithm performs better than kNN, with Recall and F1-Score of 93% and 89%, respectively, while for kNN these metrics were 92% and 85%, respectively.Repositório Científico do Instituto Politécnico de Castelo BrancoLandim, Lauriana Patricia TavaresBarata, LuísLopes, Eurico2023-04-19T14:42:15Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.11/8472engLANDIM, Lauriana ; BARATA, Luís ; LOPES, Eurico ; (2022) - A simple approach to detect anomalies in microservices-based systems using PyOD. CAPSI 2022 Proceedings. 36. ISSN 2183-489X.2183-489Xinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-11-16T01:55:00Zoai:repositorio.ipcb.pt:10400.11/8472Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-11-16T01:55Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A simple approach to detect anomalies in microservices-based systems using PyOD
title A simple approach to detect anomalies in microservices-based systems using PyOD
spellingShingle A simple approach to detect anomalies in microservices-based systems using PyOD
Landim, Lauriana Patricia Tavares
Anomaly detection
PyOD
Outliers algorithms
Microservices
title_short A simple approach to detect anomalies in microservices-based systems using PyOD
title_full A simple approach to detect anomalies in microservices-based systems using PyOD
title_fullStr A simple approach to detect anomalies in microservices-based systems using PyOD
title_full_unstemmed A simple approach to detect anomalies in microservices-based systems using PyOD
title_sort A simple approach to detect anomalies in microservices-based systems using PyOD
author Landim, Lauriana Patricia Tavares
author_facet Landim, Lauriana Patricia Tavares
Barata, Luís
Lopes, Eurico
author_role author
author2 Barata, Luís
Lopes, Eurico
author2_role author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico de Castelo Branco
dc.contributor.author.fl_str_mv Landim, Lauriana Patricia Tavares
Barata, Luís
Lopes, Eurico
dc.subject.por.fl_str_mv Anomaly detection
PyOD
Outliers algorithms
Microservices
topic Anomaly detection
PyOD
Outliers algorithms
Microservices
description Ease of scale is one of the defining characteristics of microservices. However, with scalability comes the problem of diversity of services, making it very important to detect anomalies the soonest possible. Because it is recent, there are still few studies on the best approaches to detecting anomalies in microservices. This paper proposes the Python toolkit, PyOD, as an approach for microservice anomaly detection. This toolkit is composed of a set of anomaly detection algorithms, including classical LOF (SIGMOD2000) to the latest ECOD (TKDE2022). To evaluate the approach, we used two of its algorithms, k Nearest Neighbors (kNN) and Histogram-based Outlier Score (HBOS) to detect anomalies such as application bugs, CPU exhausted, and network jam on the TraceRCA dataset. This dataset contains logs from a real microservices system. The preliminary results show that HBOS algorithm performs better than kNN, with Recall and F1-Score of 93% and 89%, respectively, while for kNN these metrics were 92% and 85%, respectively.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-01-01T00:00:00Z
2023-04-19T14:42:15Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.11/8472
url http://hdl.handle.net/10400.11/8472
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv LANDIM, Lauriana ; BARATA, Luís ; LOPES, Eurico ; (2022) - A simple approach to detect anomalies in microservices-based systems using PyOD. CAPSI 2022 Proceedings. 36. ISSN 2183-489X.
2183-489X
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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