Hadoop Based Scalable Cluster Deduplication for Big Data
Qing Liu, Yinjin Fu, Guiqiang Ni, Rui Hou · 2016
The exponential growth of data has brought a tremendous challenge on the storage system in data center. Data deduplication technology which detects and eliminates redundant data in the dataset can greatly reduce the quantity of data and optimize the utilization of storage space. This paper presented a scalable and reliable cluster deduplication system Halodedu over the Hadoop-based cloud computing platform. Halodedu used MapReduce and HDFS to realize parallel deduplication processing and manage data storage, respectively. Intra-node local database was used to build up a fast and distributed chunk fingerprint index management. In order to maintain the availability and reliability of metadata, HBase was utilized to store the metadata of backup files. We further used virtual machine images as input dataset to evaluate Halodedu. The comparative experiments demonstrated that Halodedu has improvements on deduplication speed and system scalability.