| Summary: | 碩士 === 國立成功大學 === 製造資訊與系統研究所碩博士班 === 101 === The Hadoop MapReduce is the programming model of designing the auto scalable distributed computing applications. It provides developer an effective environment to attain automatic parallelization. However, most existing manufacturing systems are arduous and restrictive to migrate to MapReduce private cloud, due to the platform incompatible and tremendous complexity of system reconstruction. For increasing the efficiency of manufacturing systems with minimum modification of existing systems, we design a framework in this thesis, called MC-Framework: Multi-users-based Cloudizing-Application Framework. It provides the simple interface to users for fairly executing requested tasks worked with traditional standalone software packages in MapReduce-based private cloud environments. Moreover, this thesis focuses on the multi-users workloads, but the default Hadoop scheduling scheme, i.e., FIFO, would increase delay under multiuser scenarios. Hence, we also propose a new scheduling mechanism, called Job-Sharing Scheduling, to explore and fairly share the jobs to machines in the MapReduce-based private cloud. This study uses an experimental design to verify and analysis the proposed MC-Framework with two case studies: (1) independent model systems include the stochastic Petri nets mode, and (2) dependence model systems include the virtual-metrology module of a manufacturing system. The results of our experiments indicate that our proposed framework enormously improved the time performance compared with the original package.
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