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This work is part of the BigStorage project, supported by the European Commission under the Marie Sklodowska-Curie Actions (H2020-MSCA-ITN-2014-642963) and it has been partly funded by the Spanish Ministry of Economy (grants TIN2014-62143-EXP, TIN2015-70799-R and TIN2016-78011-C4-4-R). Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).
Graph-based root cause analysis for service-oriented and microservice architectures
Publicated to:Journal Of Systems And Software. 159 (110432): 110432- - 2020-01-01 159(110432), DOI: 10.1016/j.jss.2019.110432
Authors: Brandon, Alvaro; Sole, Marc; Huelamo, Alberto; Solans, David; Perez, Maria S; Muntes-Mulero, Victor
Affiliations
Abstract
Service-oriented architectures and microservices define two ways of designing software with the aim of dividing an application into loosely-coupled services that communicate among each other. This translates into rapid development, where each service is developed and deployed by small teams, enabling continuous shipping of new features and fast-evolving applications. However, the underlying complexity of this type of architecture can hinder observability and maintenance by the user. In particular, identifying the root cause of an anomaly detected in the application can be a difficult and time-consuming task, considering the numerous services and connections to be examined. In this work, we present a root cause analysis framework, based on graph representations of these architectures. The graphs can be used to compare any anomalous situation that happens in the system with a library of anomalous graphs that serves as a knowledge base for the user troubleshooting those anomalies. We use the Grid'5000 testbed to deploy three different architectures and inject a set of anomalies. The results show how our graph-based approach is 19.41% more effective than a machine learning method that does not take into account the relationship between elements. (C) 2019 Elsevier Inc. All rights reserved.
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Quality index
Bibliometric impact. Analysis of the contribution and dissemination channel
The work has been published in the journal Journal Of Systems And Software due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position 27/110, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods.
From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 3.62. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)
This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:
- Weighted Average of Normalized Impact by the Scopus agency: 3.53 (source consulted: FECYT Feb 2024)
- Field Citation Ratio (FCR) from Dimensions: 24.98 (source consulted: Dimensions Jul 2025)
Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-24, the following number of citations:
- WoS: 56
- Scopus: 83
Impact and social visibility
Leadership analysis of institutional authors
There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (BRANDON HERNANDEZ, ALVARO) .
the author responsible for correspondence tasks has been BRANDON HERNANDEZ, ALVARO.