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

Landim, Lauriana Patricia Tavares; Barata, Luís; Lopes, Eurico

Publication

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

2022Journal article

dc.contributor.author	Landim, Lauriana Patricia Tavares
dc.contributor.author	Barata, Luís
dc.contributor.author	Lopes, Eurico
dc.date.accessioned	2023-04-19T14:42:15Z
dc.date.available	2023-04-19T14:42:15Z
dc.date.issued	2022
dc.description.abstract	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.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	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.	pt_PT
dc.identifier.issn	2183-489X
dc.identifier.uri	http://hdl.handle.net/10400.11/8472
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Anomaly detection	pt_PT
dc.subject	PyOD	pt_PT
dc.subject	Outliers algorithms	pt_PT
dc.subject	Microservices	pt_PT
dc.title	A simple approach to detect anomalies in microservices-based systems using PyOD	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.conferencePlace	Cabo Verde	pt_PT
oaire.citation.endPage	186	pt_PT
oaire.citation.startPage	177	pt_PT
oaire.citation.title	22.ª Conferência da Associação Portuguesa de Sistemas de Informação (CAPSI’2022)	pt_PT
person.familyName	Lopes
person.givenName	Eurico
person.identifier.ciencia-id	281C-B830-CF30
person.identifier.ciencia-id	C117-F943-3B96
person.identifier.orcid	0000-0002-6471-4681
person.identifier.orcid	0000-0002-1818-8203
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
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