Adaptive Task Scheduling Strategy Based on Dynamic Workload Adjustment for Heterogeneous Hadoop Clusters
Adaptive Task Scheduling Strategy Based on Dynamic Workload Adjustment for Heterogeneous Hadoop Clusters
复制标题
基于动态工作负载调整的异构Hadoop集群自适应任务调度策略
DOI:
10.1109/jsyst.2014.2323112
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发表时间:
2016-06
影响因子:
4.4
通讯作者:
Wang Xinhen
中科院分区:
文献类型:
--
作者:
Xu Xiaolong(徐小龙);Cao Lingling;Wang Xinhen
The original task scheduling algorithm of Hadoop cannot meet the performance requirements of heterogeneous clusters. According to the dynamic change of load of each task node and the difference of node performance of different tasks in the heterogeneous Hadoop cluster, a novel adaptive task scheduling strategy based on dynamic workload adjustment (ATSDWA) is presented. With ATSDWA, tasktrackers can adapt to the change of load at runtime, obtain tasks in accordance with the computing ability of their own, and realize the self-regulation, while avoiding the complexity of algorithm, which is the prime reason to make jobtracker the system performance bottleneck. Experimental results show that ATSDWA is a highly efficient and reliable algorithm, which can make heterogeneous Hadoop clusters stable, scalable, efficient, and load balancing. Furthermore, its performance is superior to the original and improved task scheduling strategy of Hadoop, from the aspects of the execution time of tasks, the resource utilization, and the speed-up ratio.
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