Minimizing Cost of Virtual Machines for Deadline-Constrained MapReduce Applications in the Cloud
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
Boosting energy efficiency with mirrored data block replication policy and energy scheduler
ACM SIGOPS Operating Systems Review
SHadoop: Improving MapReduce performance by optimizing job execution mechanism in Hadoop clusters
Journal of Parallel and Distributed Computing
| Hi-index | 0.00 |
MapReduce is a powerful platform for large-scale data processing. To achieve good performance, a MapReduce scheduler must avoid unnecessary data transmission by enhancing the data locality (placing tasks on nodes that contain their input data). This paper develops a new MapReduce scheduling technique to enhance map task's data locality. We have integrated this technique into Hadoop default FIFO scheduler and Hadoop fair scheduler. To evaluate our technique, we compare not only MapReduce scheduling algorithms with and without our technique but also with an existing data locality enhancement technique (i.e., the delay algorithm developed by Face book). Experimental results show that our technique often leads to the highest data locality rate and the lowest response time for map tasks. Furthermore, unlike the delay algorithm, it does not require an intricate parameter tuning process.