Incorporating Astronomical Catalog by using Cross-Matching Algorithm with MapReduce

• Main Authors: Jia-Shin Shie, 謝佳昕
• Other Authors: Meng-Feng Tsai
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/rb5939

碩士 === 國立中央大學 === 資訊工程學系 === 104 === Cross-Matching is a common way for find out the useful information from different star catalogs. Today hardware is more powerful than before. The data obtained through astronomical telescopes are becoming much larger. Therefore, single machine is not able to afford handling the astronomical data. In this paper, we use OpenStack to build a cloud computing environment, Hadoop as a distributed system, HDFS and HBase as distributed storages. Implement Cross-matching with MapReduce framework. In addition, Hbase supports random access so we make an incremental mechanism. User can update new astronomical data as they want. In the experiment, Transient is my test data to compare the operation time of using single machine with distributed system and using the same number of nodes on the physical machine with virtual machine. The result shows that using virtual machine is faster than using physical machine. Furthermore, we create 12 physical nodes on cloud environment to observe the operation time of different number of node. Theoretically, when we use more nodes to run the program the speed is much faster. The fact that the speeds of 10 nodes and 12 nodes are very similar.