A framework for the predictive monitoring and data quality assurance in the cloud continuum

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dc.contributor.authorBonilla, Lander
dc.contributor.authorDíaz de Arcaya Serrano, Josu
dc.contributor.authorLópez de Armentia Mendizabal, Juan
dc.contributor.authorAlmeida, Aitor
dc.date.accessioned2026-05-26T14:42:46Z
dc.date.available2026-05-26T14:42:46Z
dc.date.issued2026-12-01
dc.date.updated2026-05-26T14:42:46Z
dc.description.abstractThe widespread adoption of the cloud continuum paradigm poses significant challenges such as the management of heterogeneous infrastructural devices, the strict security and privacy requirements, and the complex data governance constraints. While the cloud provides access to advanced services that are often inaccessible to small and medium organizations, edge and fog resources are essential to meet latency, locality, and efficiency demands. The main benefits provided by the cloud continuum is to provide scalability, flexibility, and resilience by seamlessly integrating networking, storage, and computing resources across different layers. In this context, we present a lightweight agent, coined EdgeGuard, designed for seamless integration into heterogeneous infrastructures within a computing continuum architecture. It enables real-time monitoring of multiple metrics (e.g., resource utilization, energy consumption) and provides predictive capabilities to anticipate and mitigate potential issues before they escalate. We validate our proposal through an experimental scenario involving a diverse set of infrastructural devices distributed across the continuum with experts in the field. The evaluation shows that EdgeGuard consistently outperforms human experts across all measured metrics. These results highlight its effectiveness in proactive monitoring and correction of infrastructural issues, making it a suitable tool for modern distributed computing environments. Ultimately, EdgeGuard contributes to building more resilient, scalable, and intelligent systems within the evolving landscape of edge-cloud continuum.en
dc.description.sponsorshipThis work was supported by NexusForum.EU project, that is a Coordination & Support Action co-funded by the European Union’s Horizon Europe research and innovation programme under Grant Agreement 101,135,632 and by the Swiss State Secretariat for Education, Research, and Innovation (SERI)eng
dc.identifier.citationBonilla, L., Diaz-de-Arcaya, J., López-de-Armentia, J., & Almeida, A. (2026). A framework for the predictive monitoring and data quality assurance in the cloud continuum. Journal of Cloud Computing, 15(1). https://doi.org/10.1186/S13677-026-00881-X
dc.identifier.doi10.1186/S13677-026-00881-X
dc.identifier.eissn2192-113X
dc.identifier.urihttps://hdl.handle.net/20.500.14454/6068
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights© The Author(s) 2026
dc.subject.otherAIIoT
dc.subject.otherAIOps
dc.subject.otherCloud continuum
dc.subject.otherDistributed monitoring
dc.subject.otherHybrid cloud-edge architecture
dc.subject.otherPredictive maintenance
dc.titleA framework for the predictive monitoring and data quality assurance in the cloud continuumen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleJournal of Cloud Computing
oaire.citation.volume15
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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