MapReduce Programming Model for .NET-Based Cloud Computing
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Fast parallelization of differential evolution algorithm using MapReduce
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Executing multiple group by query using mapreduce approach: implementation and optimization
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Analysis and design insights for an E-finance platform using parallel processing
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
A library to run evolutionary algorithms in the cloud using mapreduce
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Evolutionary design of experiments using the MapReduce framework
Proceedings of the 2011 Summer Computer Simulation Conference
High performance parallel evolutionary algorithm model based on MapReduce framework
International Journal of Computer Applications in Technology
An improved partitioning mechanism for optimizing massive data analysis using MapReduce
The Journal of Supercomputing
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The MapReduce programming model allows users to easily develop distributed applications in data centers. However, many applications cannot be exactly expressed with MapReduce due to their specific characteristics. For instance, Genetic Algorithms (GAs) naturally fit into an iterative style. That does not follow the two phase pattern of MapReduce. This paper presents an extension to the MapReduce model featuring a hierarchical reduction phase. This model is called MRPGA (MapReduce for Parallel GAs), which can automatically parallelize GAs. We describe the design and implementation of the extended MapReduce model on a .NET-based enterprise Grid system in detail. The evaluation of this model with its runtime system is presented using example applications.