Governance and sustainability of distributed continuum systems: a big data approach

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• dc.contributor.author Donta, Praveen Kumar
• dc.contributor.author Sedlak, Boris
• dc.contributor.author Casamayor Pujol, Víctor
• dc.contributor.author Dustdar, Schahram
• dc.date.accessioned 2025-04-28T06:25:28Z
• dc.date.available 2025-04-28T06:25:28Z
• dc.date.issued 2023
• dc.description.abstract Distributed computing continuum systems (DCCS) make use of a vast number of computing devices to process data generated by edge devices such as the Internet of Things and sensor nodes. Besides performing computations, these devices also produce data including, for example, event logs, configuration files, network management information. When these data are analyzed, we can learn more about the devices, such as their capabilities, processing efficiency, resource usage, and failure prediction. However, these data are available in different forms and have different attributes due to the highly heterogeneous nature of DCCS. The diversity of data poses various challenges which we discuss by relating them to big data, so that we can utilize the advantages of big data analytical tools. We enumerate several existing tools that can perform the monitoring task and also summarize their characteristics. Further, we provide a general governance and sustainable architecture for DCCS, which reflects the human body’s self-healing model. The proposed model has three stages: first, it analyzes system data to acquire knowledge; second, it can leverage the knowledge to monitor and predict future conditions; and third, it takes further actions to autonomously solve any issue or to alert administrators. Thus, the DCCS model is designed to minimize the system’s downtime while optimizing resource usage. A small set of data is used to illustrate the monitoring and prediction of the performance of a system through Bayesian network structure learning. Finally, we discuss the limitations of the governance and sustainability model, and we provide possible solutions to overcome them and make the system more efficient.en
• dc.description.sponsorship Research has received funding from the EU’s Horizon Europe Research and Innovation Program under Grant Agreement No. 101070186. EU website for Teadal: https://www.teadal.eu/.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Donta PK, Sedlak B, Casamayor V, Dustdar S. Governance and sustainability of distributed continuum systems: a big data approach. J Big Data. 2023 Apr 28;10(1):53. DOI: 10.1186/s40537-023-00737-0
• dc.identifier.doi http://dx.doi.org/10.1186/s40537-023-00737-0
• dc.identifier.issn 2196-1115
• dc.identifier.uri http://hdl.handle.net/10230/70223
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Journal of Big Data. 2023 Apr 28;10(1):53
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101070186
• dc.rights This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Distributed computing continuum systemsen
• dc.subject.keyword Big data analyticsen
• dc.subject.keyword Governance and sustainabilityen
• dc.subject.keyword Self-healingen
• dc.subject.keyword Representation learningen
• dc.subject.keyword Bayesian network structure learningen
• dc.title Governance and sustainability of distributed continuum systems: a big data approachen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion