MapReduce-based Image Processing System with Priority-based DSRF Algorithm
| Autor: | Ling Shang Kuo, 郭玲裳 |
|---|---|
| Rok vydání: | 2012 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 100 MapReduce, a programming model proposed by Google, is designed for distributed parallel computing to process vast amounts of data. MapReduce users write the Map and Reduce functions, input the data to be processed and the task will be finished automatically. Hadoop, a distributed file system designed for implementing Google MapReduce, is adopted by many enterprises for daily data-intensive applications. Most users process short tasks using MapReduce; in other words, most tasks handled by the Map and Reduce functions require low response time. Currently, quite few users use MapReduce for 2D to 3D image processing, which is highly complicated and requires long execution time. However, in our opinion, MapReduce is exactly suitable for processing applications of high complexity and high computation. The other researches use MapReduce to build their applications. In the above researches, they will store the complete data into their file system. In our paper, our system is a real-time image processing system and the file system will get the real-time image continually. By the way, the system doesn’t have a schedule algorithm to solve the real-time application problem. This paper implements MapReduce on an integrated 2D to 3D multi-user system, in which Map is responsible for image processing procedures of high complexity and high computation, and Reduce is responsible for integrating the intermediate data processed by Map for the final output. Different from short tasks, when several users compete simultaneously to acquire data from MapReduce for 2D to 3D applications, data that waits to be processed by Map will be delayed by the current user and Reduce has to wait until the completion of all Map tasks to generate the final result. Therefore, a novel scheduling scheme, Dynamic Switch of Reduce Function (DSRF) Algorithm. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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