Abstract:
Fork-Join paradigm is model of parallel computing. This family of al-
gorithms divide the input into smaller pieces that are served in parallel
and then joined into a unique output. MapReduce is an example of Fork-
Join algorithm, it maps and sorts the input in smaller parts and then re-
duces them into the final result. With the classical implementations of
MapReduce we don’t have any control on the speeds used by the nodes
during the Map phase but studies [1] point out how controlling such speeds
thanks to processor frequency scaling can reduce the power-consumption
and resources of the system while maintaining a similar throughput. In
this document we study the MapReduce paradigm and analyze how vari-
ous methods of Rate control can optimize a physical implementation.
