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.

中文翻译：

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具有开放地图任务和系列减少任务的 MapReduce 调度问题的启发式算法

基于经典的 MapReduce 概念，我们提出了一种扩展的 MapReduce 调度模型。在扩展的 MapReduce 调度问题中，我们假设每个作业包含一个 open-map 任务（map 任务可以分为多个非并行操作）和 series-reduce 任务（每个 reduce 任务仅包含一个操作）。与经典的 MapReduce 调度问题不同，我们还假设所有操作不能并行处理，并且机器设置是不相关的机器。为了解决扩展的MapReduce调度问题，我们建立了一个以最小makespan为目标函数的混合整数规划模型。然后我们提出了一种遗传算法、一种模拟退火算法和一种LF算法来解决这个问题。