VázquezCaderno, PabloAwaysheh, FerasCabaleiro Domínguez, José CarlosFernández Pena, Anselmo Tomás2025-09-182025-09-182025-07-16Caderno, P.V., Awaysheh, F., Cabaleiro, J.C. et al. BigOPERA: An OPportunistic and Elastic Resource Allocation for big data frameworks. Cluster Comput 28, 383 (2025). https://doi.org/10.1007/s10586-025-05274-4https://hdl.handle.net/10347/42844Efficient asset management is essential for optimizing the performance and scalability of modern Big Data (BD) frameworks. However, traditional resource allocation methods often suffer from static partitioning, inefficient resource utilization, and high operational costs, limiting their ability to adapt to fluctuating workloads dynamically. This paper introduces BigOPERA, an opportunistic and elastic resource allocation framework designed to enhance BD processing environments by integrating dedicated and non-dedicated computing assets. Leveraging containerization and a two-tiered scheduling mechanism, BigOPERA dynamically manages available resources to improve workload execution efficiency. Experimental results demonstrate that BigOPERA achieves up to 35% performance improvement over native Apache Spark configurations, significantly enhancing computational throughput while optimizing resource consumption. Our findings highlight the potential of BigOPERA in scalable, cost-effective, and sustainable BD processing.eng(c) The Author(s) 2025. Open Access 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/.DBApache SparkOpportunistic schedulingDynamic resource provisioningResource allocationElastic computingGreen computingjournal article10.1007/s10586-025-05274-41573-7543open access