MapReduce is a crucial framework in the cloud computing architecture, and is implemented by Apache Hadoop and other cloud computing platforms. The resources required for executing jobs in a large data center vary according to the job types. In general, there are two types of jobs, CPU-bound and I/O-bound, which require different resources but run simultaneously in the same cluster. The default job scheduling policy of Hadoop is first-come-first-served and therefore, may cause unbalanced resource utilization. Considering various job workloads, numerous job allocation schedulers were proposed in the literature. However, those schedulers encountered the data locality problem or unreasonable job execution performance. This study proposes a job scheduler based on a dynamic grouping integrated neighboring search strategy, which can balance the resource utilization and improve the performance and data locality in heterogeneous computing environments.

中文翻译：

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使用动态分组集成相邻搜索的 Hadoop MapReduce 异构作业分配调度程序

MapReduce是云计算架构中的关键框架，由Apache Hadoop等云计算平台实现。在大型数据中心执行作业所需的资源因作业类型而异。一般来说，有两种类型的作业，CPU-bound 和 I/O-bound，它们需要不同的资源但在同一个集群中同时运行。Hadoop 的默认作业调度策略是先到先得，因此可能会导致资源利用率不平衡。考虑到各种工作负载，文献中提出了许多工作分配调度器。然而，这些调度器遇到了数据局部性问题或不合理的作业执行性能。本研究提出了一种基于动态分组集成相邻搜索策略的作业调度器，