RT Dissertation/Thesis
T1 BigOPERA: An OPportunistic and Elastic Resource Allocation for Big Data Frameworks
A1 Vázquez Caderno, Pablo
K1 Big Data
K1 Apache Spark
K1 Opportunistic Computing
K1 Green Computing
AB The rapid growth of data-intensive applications has driven the need for more scalable, flexible, and sustainable resource management in Big Data (BD) frameworks. Traditional computing infrastructures often rely exclusively on dedicated resources, which can lead to inefficient utilization and increased operational costs. To address this challenge, this thesis explores the integration of opportunistic computing into Apache Spark through a hybrid resource allocation framework named BigOPERA. BigOPERA combines the elasticity of opportunistic nodes, machines not primarily dedicated to the cluster, with the stability of dedicated infrastructure, achieving cost-aware scalability without compromising performance. The system architecture integrates Apache Spark in standalone mode for primary data processing, Docker for containerized task isolation, and HTCondor as the orchestrator for opportunistic resource provisioning.
YR 2025
FD 2025
LK https://hdl.handle.net/10347/42898
UL https://hdl.handle.net/10347/42898
LA eng
DS Minerva
RD 23 abr 2026