Abstract
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.
Highlights
For meeting the unpredictable demand, manufacturing companies often acquire more manufacturing equipment
The map task can be arbitrarily split into multiple map operations, and all the map operations can be processed in parallel on multiple parallel machines, while the reduce tasks are unsplittable; the reduce tasks can only be processed by one machine
When the generated random number is less than the mutation probability, based on empirical data, we divide the genetic algorithm into the following six steps to carry out chromosome mutation based on empirical data: Step 1
Summary
For meeting the unpredictable demand, manufacturing companies often acquire more manufacturing equipment. (1) We assume that the parallel machines are unrelated, while the machines are identical in the classical MapReduce model (2) In the extended MapReduce scheduling problem, map operations (i.e., a partition of the open-map task) cannot be processed in parallel. In the extended MapReduce scheduling problem, each job consists of two kinds of tasks: one open-map task and several series-reduce tasks. To address the extended MapReduce scheduling problem, we establish a mixed-integer programming model with the minimum makespan as the objective function We solve it by CPLEX, a genetic algorithm, and a simulated annealing algorithm, respectively. We consider an extended MapReduce scheduling problem with the open-map task and several series-reduce tasks In this problem, we assume that the open-map task of each job is splittable (with fixed partition pattern), and all the series-reduce tasks are unsplittable in processing. We first explain the basic symbols and decision variables and build the model
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