Online MapReduce processing on two identical parallel machines

Huang, Jidan; Zheng, Feifeng; Xu, Yinfeng; Liu, Ming

doi:10.1007/s10878-017-0167-4

Cited by 7 publications

(2 citation statements)

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“…From the small-scale instances, it can be seen that when the range of the processing time of operations is enlarged from (1,5) to (1,10), the running time of CPLEX increases significantly. While the running time of the genetic algorithm, the simulated annealing algorithm, and L-F algorithm does not change significantly.…”

Section: Small-scale Instancesmentioning

confidence: 99%

“…We compare L-F algorithm with the genetic algorithm and the simulated annealing algorithm to verify its performance. e studies of MapReduce scheduling problem often assume that all the jobs are processed on identical parallel machines [4,5]. First, in the case of online study based on this assumption, [6] considers a two-stage classical flexible flow shop problem based on MapReduce system, and give an online 6-approximation and an online (1 + ϵ)-speed O (1/ϵ 4 )competitive algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic Algorithms for MapReduce Scheduling Problem with Open-Map Task and Series-Reduce Tasks

Zheng

Wang

et al. 2020

Scientific Programming

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Based on the classical MapReduce concept, we propose an extended MapReduce scheduling model. In the extended MapReduce scheduling problem, we assumed that each job contains an open-map task (the map task can be divided into multiple unparallel operations) and series-reduce tasks (each reduce task consists of only one operation). Different from the classical MapReduce scheduling problem, we also assume that all the operations cannot be processed in parallel, and the machine settings are unrelated machines. For solving the extended MapReduce scheduling problem, we establish a mixed-integer programming model with the minimum makespan as the objective function. We then propose a genetic algorithm, a simulated annealing algorithm, and an L-F algorithm to solve this problem. Numerical experiments show that L-F algorithm has better performance in solving this problem.

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