A Comprehensive Analysis of Cloud Migration Strategies: Efficiency Comparison of Trickle, Big Bang, Refactoring, Lift and Shift, Replatforming Approaches

To resolve the poor efficiency issue encountered during database migration and ensure that the complete database is effectively transferred with less amount of time using Trickle Approach over Lift and Shift approach, Big Bang Approach, Refactoring technique, Replatforming technique. For thorough experimentation, we employ the trickle migration approach and the four traditional techniques. This experiment has a sample size of 10. The IBM Employee Dataset is used as a sample dataset, and it contains 1,059 rows of data. In this performed experimental analysis, the trickle migration approach is more efficient than Lift and Shift approach, Big Bang Approach, Refactoring technique, Replatforming technique in all aspects such as cost, time and quality of the data. The experiment shows the performance of Trickle migration approach with efficiency of (85.76%) when compared to the Big Bang Migration approach (77.49%), Refactoring technique (78.71%), Lift and Shift (76.72%), Replatforming Technique (76.96%). A significance test was performed on the five groups, resulting in a p-value of two-tailed is 0.008 for Big Bang, 0.016 for Refactoring, 0.004 for Lift and shift, 0.001 for Replatform, which is lesser than 0.05 (p<0.05) it is evident that there is an existence of statistically significant differences between these groups. The Trickle Migration approach is found to be more effective than the four traditional techniques in this research based on the data obtained.