A Comparative Analysis and Mathematical Modelling of Data Migration Tools to Evaluate Performance Parameters using Machine Learning Methods

Abstract

Abstract: Transferring data between storage systems, platforms, or places is a common reason why data movement is an important part of modern computing. The effectiveness and dependability of these tasks can be greatly affected by how well data transfer tools work. Using machine learning techniques, we compare and contrast different data transfer tools in this work. First, we will talk about some important performance metrics that are needed to properly evaluate data transfer tools. Such elements include the type of data, its size, the network's speed, and other important factors like delay and flow. Next, we get data from different data transfer tools and prepare it before reviewing their performance in a range of situations and setups. The data is analyzed using machine learning techniques like classification, regression, and grouping. Finding groups of tools that work similarly is easier with clustering. This gives you information about which tools are best for different data transfer jobs. We can describe the link between performance factors and tool performance under different situations using regression analysis. The performance traits of tools help with classification, which makes it easier to choose the right tool for a transfer job. Our findings demonstrate that machine learning techniques can correctly evaluate and contrast data transfer tools by looking at how well they do certain tasks. The paper study shows how these approaches can help companies pick the best tool for their data transfer needs by giving them useful information about tool choices. Furthermore, our research shows how important it is to use machine learning when assessing data transfer tools and sets the stage for more research to be done in this area in the future.