Aspect-combining functions for modular MapReduce solutions
Autor
Vidal Silva, Cristian
Villarroel, Rodolfo
Rubio, José
Johnson, Franklin
Madariaga, Érica
Urzúa, Alberto
Carter, Luis
Campos-Valdés, Camilo
López-Cortés, Xaviera A.
Fecha
2018Resumen
MapReduce represents a programming framework for modular Big Data computation that uses a function map to identify and target intermediate data in the mapping phase, and a function reduce to summarize the output of the map function and give a final result. Because inputs for the reduce function depend on the map function’s output to decrease the communication traffic of the output of map functions to the input of reduce functions, MapReduce permits defining combining function for local aggregation in the mapping phase. MapReduce Hadoop solutions do not warrant the combining functioning application. Even though there exist proposals for warranting the combining function execution, they break the modular nature of MapReduce solutions. Because Aspect-Oriented Programming (AOP) is a programming paradigm that looks for the modular software production, this article proposes and apply AspectCombining function, an AOP combining function, to look for a modular MapReduce solution. The Aspect-Combining application results on MapReduce Hadoop experiments highlight computing
performance and modularity improvements and a warranted execution of the combining function using an AOP framework
like AspectJ as a mandatory requisite.
Fuente
International Journal of Advanced Computer Science and Applications, 9(8), 565-574Colecciones
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