Optimizing Cloud Migration Strategies for Large-Scale Enterprises: A Comparative Analysis of Lift-and-Shift, Replatforming, and Refactoring Approaches

Abstract

Cloud migration has evolved into a pivotal strategy for large-scale enterprises seeking scalable, cost-efficient, and highly available computing solutions. As data volume grows and application demands intensify, organizations weigh multiple migration options, notably Lift-and-Shift, Replatforming, and Refactoring. Each approach offers distinct benefits and presents unique complexities in terms of operational overhead, performance optimization, and alignment with ever-evolving business objectives. This paper conducts a thorough comparative analysis of these three primary strategies, evaluating their viability based on cost modeling, performance metrics, and risk mitigation techniques. By examining real-world deployment scenarios and formulating a formal framework for assessing compatibility with enterprise goals, we derive crucial insights that inform decision-making processes. The research employs mathematical models, such as resource allocation functions for cloud-based infrastructure, and structured representations to encapsulate deployment logic, security requirements, and compliance constraints. Key considerations include latency tolerances, cost elasticity, and the capacity to integrate modern DevOps practices. Our findings illuminate how Lift-and-Shift serves as a rapid migration path, Replatforming addresses partial re-architecture, and Refactoring maximizes cloud-native capabilities. By balancing short-term gains against long-term flexibility, organizations can systematically identify the most suitable migration path. Ultimately, the comparative analysis underscores that effective cloud migration is best approached as an iterative optimization process rather than a single, static decision.