A significant amount of research in the field of job scheduling is carried out in Hadoop. However, there is still need for research to overcome some challenges regarding scheduling jobs in Hadoop clusters. There are various factors affecting the performance of scheduling policies like data volume (storage), data source format (different data), speed (data rate), security and privacy, cost, connection and data sharing. To reach a better utilization of resources and managing big data, scheduling policies have been designed. In this paper, an algorithm has been presented that can run on heterogeneous Hadoop clusters and runs job in parallel. This algorithm first distributes data based on the performance of the nodes and then schedules the jobs according to their cost of execution and decreases the cost of executing the jobs. The presented algorithm offers better performance in terms of execution time, cost and locality compared to FIFO and Fair schedulers.

中文翻译：

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用于异构 Hadoop 系统的基于单元的、经济高效的调度程序

在 Hadoop 中进行了大量作业调度领域的研究。但是，仍然需要研究以克服有关在 Hadoop 集群中调度作业的一些挑战。影响调度策略性能的因素有很多，如数据量（存储）、数据源格式（不同数据）、速度（数据速率）、安全和隐私、成本、连接和数据共享。为了更好地利用资源和管理大数据，设计了调度策略。在本文中，提出了一种可以在异构 Hadoop 集群上运行并并行运行作业的算法。该算法首先根据节点的性能分配数据，然后根据其执行成本调度作业，降低执行作业的成本。