| Summary: | 碩士 === 國立臺灣科技大學 === 電子工程系 === 100 === Cloud computing features a flexible computing infrastructure for large-scale data processing. MapReduce is becoming a leading large-scale data processing model providing a logical framework for cloud computing and Hadoop, an open-source implementation of MapReduce, is a common platform to realize such kind of parallel computing model. Nodes in the current Hadoop environment are normally homogeneous. Efficient resource management in heterogeneous clouds is crucial for improving the performance of MapReduce applications and the utilization of resources. However, the original scheduling scheme in Hadoop assign tasks to each node based on the fixed and static number of slots, without considering the physical workload of comprehensive computing resources, such as the CPU utilization, memory usuage, network bandwidth on each working node. This study aims at proposing a dynamic task allocation scheme by considering the physical workload on each node so as to prevent resource underutilization in the cloud computing environment. The evaluation results show the proposed scheme can improve the overall computation efficiency among the heterogeneous nodes in cloud.
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