Optimization of cloud resource demand forecasting and investment decisions in the context of digital transformation

With the rapid advancement of digital transformation, enterprises face escalating challenges in cloud resource allocation due to dynamic workloads and substantial capital investments. Existing forecasting models often overlook the impact of corporate digital maturity, leading to suboptimal investment decisions and resource inefficiencies. This study proposes an integrated framework combining an ARIMAX forecasting model with a multi-constraint optimization approach. We incorporate a quantified Digital Transformation Index (DTI) as an exogenous variable and develop a cost-minimization investment model under constraints including resource gaps, leasing ratios, alert thresholds, and budget limits. Simulation experiments using Alibaba Cloud cluster data demonstrate that the proposed model achieves a CPU load prediction error (MAPE) of less than 5%, with a statistically significant DTI coefficient (p < 0.01). The optimal investment strategy utilized 93.67% of a $2.22 million budget, achieving a leasing ratio below 45% while maintaining a 67% resource utilization safety threshold. We employed Mean Absolute Percentage Error (MAPE) for forecasting accuracy and Net Present Value (NPV) for cost evaluation, selected for their relevance to operational and financial performance in cloud resource management.