Flame-MR is a MapReduce framework which improves the performance of Hadoop applications. It employs several kinds of optimizations, like avoidance of memory copies, efficient sort and merge algorithms and flexible use of resources. Moreover, its event-driven architecture overlaps the data transferring and processing. Flame-MR also keeps binary compatibility with Hadoop, so applications do not have to be modified or recompiled to be executed. The experimental results show that Flame-MR can reduce the execution time of iterative workloads by a half. Further information about Flame-MR can be found in [1], [2], [3] and in the More Information section.

Citation

If you have used Flame-MR in your research, please cite our work using the following reference:

• [1] 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

References

• [2] Jorge Veiga, Roberto R. Expósito, Guillermo L. Taboada and Juan Touriño. Enhancing in-memory efficiency for MapReduce-based data processing. Journal of Parallel and Distributed Computing, vol. 120, pages 323-338. October 2018. Preprint Online
• [3] Jorge Veiga, Roberto R. Expósito, Bruno Raffin and Juan Touriño. Optimization of real-world MapReduce applications with Flame-MR: practical use cases. IEEE Access, vol. 6, pages 69750-69762. November 2018. Preprint Online