Privacy Preserving Rack-based Dynamic Workload Balancing for Hadoop MapReduce
Hadoop has two components namely HDFS and MapReduce. Hadoop stores user data based on space utilization of datanodes on the cluster rather than the processing capability of the datanodes. Furthermore Hadoop runs in a heterogeneous environment as all datanodes may not be homogeneous. For these reasons, workload imbalances will occur when jobs run in a Hadoop cluster resulting in poor performance. In this paper, we propose a dynamic algorithm to balance the workload between different racks on a Hadoop cluster based on the log files of Hadoop.
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