MapReduce is a distributed and parallel computing model for data-intensive tasks with features of optimized scheduling, flexibility, high availability, and high manageability. MapReduce can work on various platforms; however, MapReduce is not suitable for iterative programs because the performance may be lowered by frequent disk I/O operations. In order to improve system performance and resource utilization, we propose a novel MapReduce framework named Dynamically Iterative MapReduce (DIMR) to reduce numbers of disk I/O operations and the consumption of network bandwidth by means of using dynamic task allocation and memory management mechanism. We show that DIMR is promising with detail discussions in this paper.
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網際網路技術學刊=Journal of Internet Technology 14(6),頁 953-962