RT Journal Article
T1 BigOPERA: An OPportunistic and Elastic Resource Allocation for big data frameworks
A1 VázquezCaderno, Pablo
A1 Awaysheh, Feras
A1 Cabaleiro Domínguez, José Carlos
A1 Fernández Pena, Anselmo Tomás
K1 DB
K1 Apache Spark
K1 Opportunistic scheduling
K1 Dynamic resource provisioning
K1 Resource allocation
K1 Elastic computing
K1 Green computing
AB Efficient 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.
PB Springer Nature
YR 2025
FD 2025-07-16
LK https://hdl.handle.net/10347/42844
UL https://hdl.handle.net/10347/42844
LA eng
NO Caderno, 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/s10586-025-05274-4
NO Open access funding provided by Umea University. This work has received financial support from the Agencia Estatal de Investigación (Spain) (PID2022-141623NB-I00), the Xunta de Galicia - Conselleria de Educación, Ciencia, Universidades e Formación Profesional (Centro de investigación de Galicia accreditation 2024-2027 ED431G-2023/04) and Reference Competitive Group accreditation ED431C-2022/016) and the European Union (European Regional Development Fund - ERDF).
DS Minerva
RD 4 may 2026