25/11/2016 NEW: BDEv 2.3 is released! Check out the News section.
BDEv is a tool to evaluate Big Data processing solutions in terms of performance and resource efficiency. It includes several ready-to-use frameworks (e.g. Hadoop, Spark, Flink) and manages the configuration needed to leverage the available computational resources, like CPU, memory and network interfaces. The evaluation of these frameworks can be done by using different benchmarks (e.g. TeraSort, WordCount) included in the BDEv distribution, while also enabling the execution of custom commands. Moreover, BDEv eases the execution of experiments and the task of recovering results by providing automatically generated graphs.
BDEv has evolved from MREv , which was originally aimed to evaluate HPC-oriented MapReduce frameworks. MREv has been used for research purposes in , which analyses the behaviour of HPC-oriented MapReduce frameworks on an HPC cluster. It has also been used in the evaluation of Flame-MR , an efficient MapReduce framework that improves the performance of Hadoop, and for comparing the performance of Hadoop, Spark and Flink in .
-  Jorge Veiga, Roberto R. Expósito, Guillermo L. Taboada, Juan Touriño. MREv: an Automatic MapReduce Evaluation Tool for Big Data Workloads. In Proceedings of the International Conference on Computational Science (ICCS'15), vol. 51, pages 80–89. Reykjavík, Iceland, 2015. Preprint Online
-  Jorge Veiga, Roberto R. Expósito, Guillermo L. Taboada, Juan Touriño. Analysis and evaluation of MapReduce solutions on an HPC cluster. Computers & Electrical Engineering, vol. 50, pages 200-216. February 2016. Preprint Online
-  Jorge Veiga, Roberto R. Expósito, Guillermo L. Taboada, Juan Touriño. Flame-MR: An event-driven architecture for MapReduce applications. Future Generation Computer Systems, vol. 65, pages 46-56. December 2016. Preprint Online
-  Jorge Veiga, Roberto R. Expósito, Xoán C. Pardo, Guillermo L. Taboada and Juan Touriño. Performance Evaluation of Big Data Frameworks for Large-Scale Data Analytics. In Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), in press. December 2016. Preprint