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Distributed Scheduling Extension on Hadoop

机译：Hadoop上的分布式调度扩展

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摘要

Distributed computing splits a large-scale job into multiple tasks and deals with them on clusters. Cluster resource allocation is the key point to restrict the efficiency of distributed computing platform. Hadoop is the current most popular open-source distributed platform. However, the existing scheduling strategies in Hadoop are kind of simple and cannot meet the needs such as sharing the cluster for multi-user, ensuring a concept of guaranteed capacity for each job, as well as providing good performance for interactive jobs. This paper researches the existing scheduling strategies, analyses the inadequacy and adds three new features in Hadoop which can raise the weight of job temporarily, grab cluster resources by higher-priority jobs and support the computing resources share among multi-user. Experiments show they can help in providing better performance for interactive jobs, as well as more fairly share of computing time among users.
机译：分布式计算将大型工作分解为多个任务，并在集群上进行处理。集群资源分配是限制分布式计算平台效率的关键。 Hadoop是当前最流行的开源分布式平台。但是，Hadoop中现有的调度策略很简单，无法满足诸如为多用户共享集群，确保为每个作业保证容量的概念以及为交互式作业提供良好性能的需求。本文研究了现有的调度策略，分析了不足之处，并在Hadoop中添加了三个新功能，这些功能可以暂时增加工作量，通过优先级较高的工作抢占群集资源，并支持多用户之间的计算资源共享。实验表明，它们可以为交互式作业提供更好的性能，并在用户之间更公平地分配计算时间。

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