Cited by 125Software System Performance and ReliabilityAdvanced Software Engineering MethodologiesSoftware Testing and Debugging Techniques

Graph-based root cause analysis for service-oriented and microservice architectures

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Brandón, Álvaro, Solé, Marc, Huélamo, Alberto, Solans, David, Pérez, María S., Muntés-Mulero, Victor·2020·Journal of Systems and Software

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Crossref · 10.1016/j.jss.2019.110432

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71 of 71 references verified0 citation errors · 0 not checkable

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  • Graph-based approaches are widely applied to anomaly detection and root-cause identification in microservices, as they capture the complex interdependencies among services and enable structured analysis of propagation patterns.

    “Graph-based approaches are widely applied to anomaly detection and root-cause identification in microservices, as they capture the complex interdependencies among services and enable structured analysis of propagation patterns [1].”

    From Anomaly detection and root-cause identification in microservices: a survey (2026) · cites this paper as [1]

    The Abstract and Discussion describe graph representations of interconnected microservices for root-cause analysis, including anomalies that propagate to other elements. But the framework analyzes anomalies after they are detected; the paper does not establish that graph-based approaches are widely applied to both anomaly detection and root-cause identification in microservices. (LLM, conf=0.85)

    Experimental — accuracy not yet measured
  • While ML approaches provide powerful tools for anomaly detection and root-cause analysis, they face challenges of explainability and data availability in fast-evolving microservice environments.

    “While they provide powerful tools for anomaly detection and root-cause analysis, they face challenges of explainability and data availability in fast-evolving microservice environments [1].”
  • Graph-based methods capture structural and causal relationships effectively, but may be overly complex for isolated failures where simpler techniques may be more efficient.

    “While graph-based methods capture structural and causal relationships effectively, they may be overly complex for isolated failures (e.g., single node anomalies) where simpler techniques may be more efficient [1].”

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1 of the 72 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. A. Leung, A. Spyker, T. Bozarth, Titus: introducing containers to the netflix cloud, Communications of the ACM 61 (2) (2018) 38-45.
  2. M. A. Marvasti, A. V. Poghosyan, A. N. Harutyunyan, N. M. Grigoryan, An anomaly event correlation engine: Identifying root causes, bottlenecks, and black swans in it environments, VMware Technical Journal 2 (1) (2013) 35-45.
  3. J. Boyan, A. W. Moore, Learning evaluation functions to improve optimization by local search, Journal of Machine Learning Research 1 (Nov) (2000) 77-112.

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Authors
Brandón, Á., Solé, M., Huélamo, A., Solans, D., Pérez, M. S., & Muntés-Mulero, V.
Affiliations
Universidad Politécnica de Madrid (ES)
Corresponding author
Brandón, Álvaro
Venue
Journal of Systems and Software
Publisher
Elsevier BV
Published
2020-01
Volume
159
Pages
110432
ISSN
0164-1212 (ISSN-L)
OpenAlex
W2980932398
Cited by
121
References
72
Citations by year
2026: 15 · 2025: 17 · 2024: 30 · 2023: 20 · 2022: 16 · 2021: 18 · 2020: 5
FWCI
5.34
Type
journal article
Language
en
Open access
Locations
  • Journal of Systems and Software (publishedVersion)
  • UPM Digital Archive (Technical University of Madrid) (submittedVersion)
Funders
European Commission
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
  • Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
Keywords
root cause analysis, service-oriented architecture, microservices, graph representation, anomaly detection, troubleshooting, software observability, graph comparison, distributed systems

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