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A Delay Scheduling Algorithm Based on History Time in Heterogeneous Environments

机译:异构环境中基于历史时间的时延调度算法

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The MapReduce framework was designed for data-intensive computing. Many users share the Hadoop cluster becomes popular in many companies recent years. So an efficient scheduling algorithm which can balance the utilization and the parallelism of the hadoop cluster becomes very important. The Hadoop's scheduler (Fair scheduler, Delay scheduler) for multi-user cluster was designed for homogeneous environment, and works poor in heterogeneous environment. In this paper, we propose a new scheduling algorithm for multi-user Hadoop cluster, taking the history time of the completed tasks and the Delay scheduler's strategy into the algorithm, expecting achieve good performance while guaranteeing fairness in shared heterogeneous environment. Our algorithm is implemented in hadoop 0.21.1, and the experiment demonstrates the validation of our algorithm.
机译:MapReduce框架旨在用于数据密集型计算。许多用户共享Hadoop集群近年来在许多公司中变得越来越流行。因此,一种能够平衡Hadoop集群的利用率和并行性的高效调度算法变得非常重要。用于多用户集群的Hadoop调度程序(Fair调度程序,Delay调度程序)是为同类环境设计的,并且在异构环境下工作性能较差。在本文中,我们提出了一种新的多用户Hadoop集群调度算法,将已完成任务的历史时间和Delay调度程序的策略纳入该算法,以期在保证共享异构环境公平性的同时达到良好的性能。我们的算法是在hadoop 0.21.1中实现的,实验证明了该算法的有效性。

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