DATA CHUNK SIMILARITY BASED COMPRESSION APPROACH FOR BIG SENSING DATA IN CLOUD
Author’s Name : Seljo Jose | T B Dharmaraj
Volume 05 Issue 02 Year 2018 ISSN No: 2349-3828 Page no: 14-16
Cloud computing provides a promising platform for big sensing data processing and storage as it provides a flexible stack of massive computing, storage, and software services in a scalable manner. Based on specific on-Cloud data compression requirements, we propose a novel scalable data compression approach based on calculating similarity among the partitioned data chunks. The main objective is to design a load rebalancing algorithm to reallocate file chunks such that the chunks can be distributed to the system as uniformly as possible while reducing the movement cost as much as possible. First process is to allocate the chunks of files as uniformly as possible among the nodes such that no node manages an excessive number of chunks
Big Sensing Data, Cloud Computing, Data Chunk, Data Compression, Similarity Model, Scalability, Load Balancing
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