ITM Web Conf.
Volume 11, 20172017 International Conference on Information Science and Technology (IST 2017)
|Number of page(s)||8|
|Section||Session X: Big Data Processing and Management|
|Published online||23 May 2017|
Efficient Data Integrity Verification Using CRC Based on HDFS in Cloud Storage
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China
a Corresponding author: email@example.com
Data integrity verification is becoming a major challenge in cloud storage which can’t be ignored. This paper proposes an optimized variant of CRC (Checker Redundancy Cyclic) verification algorithm based on HDFS to improve the efficiency of data integrity verification in cloud storage through the research of CRC checksum algorithm and data integrity verification mechanism of HDFS. A new method is formulated to establish the deformational optimization and to accelerate the algorithm by researching characteristics of generating and checking the algorithm. Moreover, this method optimizes the code to improve the computational efficiency according to data integrity verification mechanism of HDFS. A data integrity verification system based on Hadoop is designed to verify proposed method. Experimental results demonstrate that proposed HDFS based CRC algorithm was able to improve the calculation efficiency and the utilization of system resource on the whole and outperformed well compared to existing models in terms of accuracy and time.
© Owned by the authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.