Energy-efficient Task Scheduling Model based on MapReduce for Cloud Computing using Genetic Algorithm

High energy consumption of data centers hasbecome a great obstacle to the development of cloud computing.This paper mainly focuses on how to improve theenergy efficiency of servers in a data center by appropriatetask scheduling strategies. Based on MapReduce, Google’smassive data processing framework, a new energy-efficienttask scheduling model is proposed in this paper. To solvethis model, we put forward an effective genetic algorithmwith practical encoding and decoding methods and speciallydesigned genetic operators. Meanwhile, with a view toaccelerating this algorithm’s convergent speed as well asenhancing its searching ability, a local search operator isintroduced. Finally, the experiments show that the proposedalgorithm is effective and efficient.

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