Dr. Migrate: An AI-Driven Framework for Optimized VMware-to-Azure Cloud Migration

Authors: Venkata Raman Immidisetti

DOI: https://doi.org/10.5281/zenodo.15054589

Short DOI: https://doi.org/g8837r

Country: USA

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Abstract: Cloud migration presents several challenges, including complex dependency mapping, cost optimization, and risk mitigation. Dr. Migrate is an AI-driven migration tool that automates these processes to streamline transitions from on-premises environments to Microsoft Azure. This paper explores Dr. Migrate’s architecture, methodology, and role in structured cloud migration planning and execution. The tool facilitates application dependency mapping, applies the Six R migration strategy for workload classification, and organizes wave-based phased migration planning. Additionally, it conducts total cost of ownership (TCO) analysis, manages inventory categorization, and enables performance-based rightsizing. The execution phase of cloud migration, including pre-migration testing, cutover strategies, and post-migration validation, is also discussed in detail. The benefits of using Dr. Migrate, such as improved migration timelines, cost savings, and enhanced planning accuracy, are examined alongside potential challenges, including bandwidth constraints and legacy system modernization. The paper concludes with insights into how Dr. Migrate significantly reduces migration complexity and explores future enhancements, such as AI-driven automation, that can further improve cloud transformation processes.

Keywords: Cloud Migration, Microsoft Azure, Azure Migrate, Application Dependency Mapping, Six R Strategy, Wave Planning, Total Cost of Ownership, Performance Rightsizing, AI-Driven Migration, VMware-to-Azure Migration

Paper Id: 232256

Published On: 2024-12-10

Published In: Volume 12, Issue 6, November-December 2024

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