{"id":13605,"date":"2025-10-16T13:30:10","date_gmt":"2025-10-16T11:30:10","guid":{"rendered":"https:\/\/www.ibisc.univ-evry.fr\/?page_id=13605"},"modified":"2025-10-16T13:31:04","modified_gmt":"2025-10-16T11:31:04","slug":"advance-2025-technical-session-3-full-papers-fp3-2","status":"publish","type":"page","link":"https:\/\/www.ibisc.univ-evry.fr\/en\/advance-2025-technical-session-3-full-papers-fp3-2\/","title":{"rendered":"ADVANCE 2025 – Technical Session 3 (Full Papers) – FP3.2"},"content":{"rendered":"
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Upload PDF<\/strong><\/span><\/a><\/p>\n<\/div>

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ADVANCE 2025 Web site<\/strong><\/a><\/p>\n<\/div>

ADVANCE 2025 Proceedings<\/strong><\/a><\/p>\n<\/div>

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Data Fusion of Observability Signals to Detect Anomalies and Failures in Microservices<\/strong><\/h2>\n

Adalberto F. P. Chaves\u00b9, Francisco A. A. Gomes\u00b2, Paulo A. L. Rego\u00b3, Fernando A. M. Trinta\u2074, Jos\u00e9 N. de Souza\u2075<\/p>\n

\u00b9 Federal University of Cear\u00e1 (UFC), Crate\u00fas, Cear\u00e1, Brazil (Email: felipe.pinheiro.c27@gmail.com<\/em><\/a>) \u00b2 Federal University of Cear\u00e1 (UFC), Crate\u00fas, Cear\u00e1, Brazil (Email: almada@crateus.ufc.br<\/em><\/a>) \u00b3 Federal University of Cear\u00e1 (UFC), Fortaleza, Cear\u00e1, Brazil (Email: paulo@dc.ufc.br<\/em><\/a>) \u2074 Federal University of Cear\u00e1 (UFC), Fortaleza, Cear\u00e1, Brazil (Email: fernando.trinta@dc.ufc.br<\/em><\/a>) \u2075 Federal University of Cear\u00e1 (UFC), Fortaleza, Cear\u00e1, Brazil (Email: neuman@ufc.br<\/em><\/a>)<\/p>\n

DOI: https:\/\/doi.org\/10.48545\/advance2025-fullpapers-3_2<\/a><\/p>\n

Abstract<\/strong><\/p>\n

Microservices have become a reality, with large companies adopting this architecture due to its advantages, such as development agility, decoupling, scalability, resilience, and operational efficiency. However, this approach has drawbacks, such as the need for complex cloud infrastructure and communication between microservices through protocols. In organizations with hundreds of microservices, any failure in one of them can trigger cascading effects, resulting in more failures. Identifying failures in microservices is not a trivial task. The proposed work focuses on data fusion for detecting failures and anomalies in microservices, combining two pillars of observability: distributed tracing and metrics, with the help of OpenTelemetry. This fusion aims to provide faster and more efficient failure detection, as well as the identification of anomalies in metrics. This allows problems to be detected and addressed more quickly, improving the overall stability and reliability of the system.<\/p>\n

Keywords<\/strong><\/p>\n

microservices, failures, anomalies, observability, data fusion<\/p>\n<\/div>

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