Predictive Modeling for Cloud Migration Costs: A Machine Learning Approach to Estimating Total Cost of Ownership for Enterprises

Abstract

Cloud computing continues to gain traction among enterprises aiming to enhance agility, reduce operational overhead, and accelerate innovation. However, accurately forecasting the overall expense of migrating diverse workloads remains a significant challenge for strategic decision-makers. Traditional models struggle to handle rapidly changing infrastructure configurations, pricing fluctuations, and the complex, multi-dimensional nature of cost components such as data transfer, resource provisioning, and service-level agreements. This paper introduces an approach that integrates advanced learning techniques with structured decomposition of cost factors to enhance prediction accuracy and reliability. Our framework leverages machine learning architectures capable of capturing temporal dependencies and accounting for interdependencies among operational, infrastructural, and hidden variables. Through extensive experimentation on a large dataset of enterprise migration scenarios, the proposed model demonstrates notable improvements in error metrics compared to standard forecasting baselines. An additional strength of this framework lies in its ability to surface meaningful explanations, thereby enabling stakeholders to identify critical cost drivers and mitigate risks by applying appropriate allocation or architectural changes. The research further incorporates budget-aware constraints to reconcile accuracy with enterprise financial objectives. The results suggest that systematically integrating price volatility, workload elasticity, and dependency structures can yield more reliable cost estimations and reduce the likelihood of unanticipated budget overruns.