Distributed Data Compression using Cloud Computing Approach

Abstract

Storage and data trafficking have grown a great deal over the past decade. Therefore, there is a need to reduce the size of the data for better speed and proper utilization of the bandwidth. There are compressions that happen online as well as some happen statically. The online based compression is in greater demand, since an online platform is much easier and faster. Static methods have a requisition of the entire file to be present at the time of transmission, while online compressions don’t. Compressions make use of an algorithm to effectively shrink the data. Depending upon the speed of compression and the data loss, we can determine whether the compression algorithm is good or not. Data compression can be used on any form of data. Be it images, or videos to simple text files, we can compress the data. Compression mainly involves removing the redundancy in the data. Depending on the redundancy, a text file can be reduced up to 50% or more than its original size. There are many forms of compression. Few examples are: ZIP, RAR, GZIP, PNG, JPEG, WAV, etc. There are two methods of compression. A loss less compression and a lossy compression. Few algorithms that are most widely used in lossless compression is Huffman encoding and LZ77 encoding. In this study, we hope to come up with a better compression algorithm using modern approaches and tools using Singular Value Decomposition technique.