Examinando por Autor "Bonilla, Lander"

Mostrando 1 - 4 de 4
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    AI-driven self-healing across the edge–cloud continuum: a systematic literature review
    (Elsevier B.V., 2026-10-01) Bonilla, Lander; Díaz de Arcaya Serrano, Josu; Aguirre Usandizaga, Jon; Almeida, Aitor
    Context: The increasing complexity posed by Edge–Cloud Continuum architectures requires innovative software development practices and paradigms. Traditional software engineering practices fall short for addressing the particularities of such deployments; hence, there is a critical need for innovative tools and techniques capable of autonomous self-remediation and enhanced resilience with minimal human intervention. Objective: The overarching goal of this study is to synthesize the state-of-the-art with regard to AI-driven self-healing in the computing continuum. Implementation challenges and ethical implications are analyzed to identify effective approaches, architectures, and future trends in the field of automated program repair. Methods: A Systematic Literature Review was conducted following the PRISMA 2020 guidelines and Kitchenham’s methodology for software engineering. A rigorous automated search across designated academic databases initially yielded 2930 records. After applying strict inclusion and exclusion criteria, 99 primary studies were selected. Results: The analysis reveals a fundamental paradigm shift from observational monitoring to actionable, autonomous repair. While LLM-driven approaches and Agentic frameworks demonstrate high efficacy in tasks such as causal discovery and complex code patching, a critical validation gap remains regarding their deployment in non-deterministic runtime environments. Current literature often lacks the rigorous testing frameworks required to ensure that AI-generated repairs do not introduce regressions or security vulnerabilities. Conclusion: Achieving trustworthy autonomy requires a holistic approach that merges advanced AI paradigms with disciplined software engineering. Future research must prioritize the creation of standardized benchmarks and empirical studies to bridge the gap between theoretical algorithms and industrial application. Furthermore, the development of decentralized multi-agent architectures and Human-in-the-Loop techniques is essential to ensure safety, explainability, and ethical compliance in the next generation of resilient software systems.
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    ArtifactOps and ArtifactDL: a methodology and a language for conceptualizing and operationalising different types of pipelines
    (Springer Science and Business Media Deutschland GmbH, 2025-08-07) Miñón Jiménez, Raúl; Díaz de Arcaya Serrano, Josu; Torre Bastida, Ana Isabel; López de Armentia Mendizabal, Juan; Zárate Martínez, Gorka; Bonilla, Lander; Garcia Perez, Asier; Aguirre Usandizaga, Jon
    Machine learning is already integrated in diverse domains enhancing their performance and decision support. For laboratories, this approach is normally sufficient. However, in real environments, these models can not be generally deployed isolated since they require additional steps to satisfy an objective. These steps can range from different data transformations to the inclusion of extra machine learning models which compose an analytic pipeline. Moreover, the majority of software solutions wrap a model into an API and, rarely, focus on the whole pipeline. These are unresolved topics in the well-known MLOps methodology, specifically in packaging and service phases. In addition, these concerns can also be extrapolated to other paradigms like DevOps or DataOps. In the context of the Pliades European project, this paper approaches the conceptualization of diverse types of pipelines from different perspectives and for different contexts, instead of simplifying the deployment and serving to an API. Thus, ArtifactOps methodology is proposed aimed at unifying XXOps paradigms which share the majority of stages. Finally, ArtifactDL pipeline definition language is proposed to describe the key aspects identified when designing different pipelines types and to support the proposed ArtifactOps methodology. Moreover, the research presents two real scenarios to better illustrate both ArtifactOps methodology and ArtifactDL pipeline definition language and it is defined an expert evaluation conducted to validate the approach.
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    A framework for the predictive monitoring and data quality assurance in the cloud continuum
    (Springer Science and Business Media Deutschland GmbH, 2026-12-01) Bonilla, Lander; Díaz de Arcaya Serrano, Josu; López de Armentia Mendizabal, Juan; Almeida, Aitor
    The 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.
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    A multi-level IIOT platform for boosting mines digitalization
    (Elsevier B.V., 2025-02) Miñón Jiménez, Raúl; López de Armentia Mendizabal, Juan; Bonilla, Lander; Brazaola, Aitor; Laña Aurrecoechea, Ibai; Palacios Prados, María Carmen; Mueller, Szymon; Blaszczak, Michal; Zeiner, Herwig; Tschuden, Julia; Quadri, Mohammad Yusuf; Garcia-Milà, Ignasi; Bartoli, Andrea; Gormolla, Norbert; Fernández Osorio, Alberto; Segarra Catasus, Pablo; Sanchidrián Blanco, José Ángel; Hartlieb, Philipp
    This paper presents an innovative IIoT multi-level platform tailored to address the specific needs of the mining domain. The platform has been conceptualized and built in the context of the illuMINEation European project. For this purpose, mining specific use cases have been designed such as promoting underground safe areas, performing efficient mining operations or boosting predictive maintenance approaches. Then, specific requirements have been identified and, as a result, the platform has been developed. It consists of four-level layered platform: (1) edge devices layer to manage several sensors deployed in the mines; (2) edge box layer to provide in-mine operations such as filtering, streaming and processing; (3) fog layer which offers an overall perspective of each mine; and (4) cloud layer to centralize the data of all the mines and to provide powerful processing capabilities. In addition, the platform is robustly secured in terms of protecting communications confidentiality and access control and also provides a toolbox aimed at manipulating 3D complex images to obtain operable mine-domain novel user interfaces. Finally, a platform validation is proposed where three different use cases are explained to better show and demonstrate the capabilities of the platform.