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MapReduce optimization using regulated dynamic prioritization

MapReduce optimization using regulated dynamic prioritization,10.1145/1555349.1555384,Thomas Sandholm,Kevin Lai

MapReduce optimization using regulated dynamic prioritization   (Citations: 22)
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We present a system for allocating resources in shared data and compute clusters that improves MapReduce job scheduling in three ways. First, the system uses regulated and user-assigned priorities to offer different service levels to jobs and users over time. Sec- ond, the system dynamically adjusts resource allocations to fit the requirements of different job stages. Finally, the system automat- ically detects and eliminates bottlenecks within a job. We show experimentally using real applications that users can optimize not only job execution time but also the cost-benefit ratio or prioritiza- tion efficiency of a job using these three strategies. Our approach relies on a proportional share mechanism that continuously allo- cates virtual machine resources. Our experimental results show a 11−31% improvement in completion time and 4−187% improve- ment in prioritization efficiency for different classes of MapReduce jobs. We further show that delay intolerant users gain even more from our system.
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