Elastic phoenix: malleable mapreduce for shared-memory systems
NPC'11 Proceedings of the 8th IFIP international conference on Network and parallel computing
Improving Hadoop performance in intercloud environments
ACM SIGMETRICS Performance Evaluation Review
A library to run evolutionary algorithms in the cloud using mapreduce
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Breaking the MapReduce stage barrier
Cluster Computing
MapReduce with communication overlap (MaRCO)
Journal of Parallel and Distributed Computing
SIDR: structure-aware intelligent data routing in Hadoop
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
A MapReduce task scheduling algorithm for deadline constraints
Cluster Computing
| Hi-index | 0.00 |
The MapReduce model uses a barrier between the Map and Reduce stages. This provides simplicity in both programming and implementation. However, in many situations, this barrier hurts performance because it is overly restrictive. Hence, we develop a method to break the barrier in MapReduce in a way that improves efficiency. Careful design of our barrierless MapReduce framework results in equivalent generality and retains ease of programming. We motivate our case with, and experimentally study our barrier-less techniques in, a wide variety of MapReduce applications divided into seven classes. Our experiments show that our approach can achieve better performance times than a traditional MapReduce framework. We achieve a reduction in job completion times that is 25% on average and 87% in the best case.