A Regression-based Hybrid Machine Learning Technique to Enhance the Database Migration in Cloud Environment

The report of cloud computing in recent years has prompted circumstances that usually has lead to numerous advancements & novel mechanisms. The technologies available in the cloud have been prevalent for businesses as well as people who understand that cloud computing is a significant problem, even though they don't know why. We present a methodology that accurately assesses the migration cost, relocation length with cloud operating cost of the social databases, and upgraded the execution. The first step in our approach is to acquire workloads and structure models for moving the database from the database logs as well as from schemes. The second step uses these models to perform a discrete form of event simulation for estimated costs and time. We have implemented the software tools that simplify our approach in both phases. A comprehensive review contrasts our approach to the effects of real-world cloud data migration.