Normalized Comparison Method for Finding the Most Efficient DSS Code

Abstract

Big data is large volume of data produced on a daily basis. Distributed storage systems (DSS) is environment that handles, manages and stores those data. The main drawbacks are the lack of system storage capacity, the network device failures that can appear anytime, on time data processing and the system efficiency. All above mentioned issues can be overcome by applying different coding techniques for data distribution. Till this moment many coding schemes are proposed by the researchers. Determining the most efficient code for usage is still a tricky question, which yields an adequate comparison strategy for code selection. The basic Dimakis comparison method offers analysis between the codes regarding the parameters storage per node and download bandwidth to be repair one node. Total comparison method includes in the analysis the total number of nodes in the system together with the overall storage and total downloaded bandwidth in the repair process, with notation that the file size for all codes must be same. In this paper, we are proposing new method for comparison, called Normalized, that enables consideration of broader spectrum of parameters and not necessarily the same file size of the proposed codes.