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机译:YARN上用于Spark和MapReduce的跨平台资源调度
Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC;
Department of Computer Science, University of Colorado, Colorado Springs, CO;
Department of Computer Science, University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX;
Department of Computer Science & Technology, Tongji University, 1239 Siping Road, Shanghai, China;
Department of Computer Science & Technology, Tongji University, 1239 Siping Road, Shanghai, China;
Sparks; Resource management; Yarn; Job shop scheduling; Computer science; Processor scheduling; Big data;
机译:约束编程与Hadoop Yarn中Mapreduce调度问题的启发式方法,以实现能量最小化
机译:具有动态资源可用性的可感知截止日期的MapReduce作业调度
机译:TaskTracker意识到Hadoop MapReduce的资源可用性控件的调度程序
机译:MapReduce和Spark纱线决策树研究
机译:MapReduce群集中的调度
机译:并行MapReduce:使用并行执行策略来最大程度地利用云资源并提高性能
机译:基于两遍调度策略的MapReduce资源分配