Authors: Mohana Ravi Teja Gutta
Abstract: Data migration of large volumes of data from legacy information systems to cloud-based architecture is becoming more and more common in enterprise digital transformation projects, traditional data migration methods usually suffer from issues related to the use of disparate data format, incompatibility of data schema, migration downtime, inefficient resource utilization, and insufficient data validation. These issues lead to lengthy migration process, higher migration risks, and lower migration performance. This research proposes a Cloud Native Enterprise Data Migration Framework (CN-EDMF) that combines data discovery, data dependencies, data harmonization, workload management, resource management, and data validation in one migration framework. The proposed framework employs metadata-based intelligence for identifying relationships within enterprise datasets, determining optimal migration sequence, and allocating cloud resources according to the characteristics of workloads. Moreover, automated mechanisms for ensuring data quality and data integrity verify that the process is executed successfully and reliably. The framework was evaluated experimentally using enterprise datasets varying between 5 TB to 25 TB and was compared with other existing frameworks including ServiceMiner (SM) and CloudNet (CN). As a result, CN-EDMF has managed to achieve the following results Migration Success Rate of 99.2%, Data Quality Preservation of 99.4%, Migration Reliability of 99.6%, and Resource Utilization Efficiency of 94.4%. The research proves the effectiveness of the proposed framework for improving the migration process. It provides an efficient solution for cloud-native enterprise migration.
DOI: https://zenodo.org/records/22957355
