Cost-Performance Optimization in Hybrid Cloud Deployment Models

Abstract

The hybrid cloud deployment model, integrating private and public cloud infrastructures, has become a cornerstone of modern IT strategy, enabling organizations to balance cost efficiency, performance, and regulatory compliance. However, optimizing this balance remains a multidimensional challenge involving workload placement, scaling policies, and dynamic cost models. This research presents an integrated approach combining predictive workload modeling, intelligent resource allocation, and simulation-based validation to address cost–performance trade-offs in hybrid environments. Drawing upon a comprehensive literature review, the study identifies key parameters influencing hybrid cloud economics, including compute cost variability, data egress fees, storage tiering, and network latency.
The methodology leverages workload profiling, a dual cost–performance model, and CloudSim-based simulations to compare threshold-based and machine learning (ML)-driven scaling strategies. Statistical analysis of simulated workloads (n=120 test runs) shows that the ML-based scaling approach yields an average 27% cost reduction, 22.9% improvement in average response times, and 25.9% increase in resource utilization, with all results statistically significant at p < 0.05. Notably, SLA compliance improved by 4.1%, demonstrating that cost savings did not come at the expense of service quality.

These findings contribute to both academic research and industry practice by providing a reproducible optimization framework. The results are particularly relevant for organizations experiencing unpredictable workloads, such as e-commerce platforms, financial analytics services, and computational research clusters. This work further highlights that hybrid cloud cost–performance optimization is not solely a technical exercise but also a strategic decision-making process requiring continuous monitoring and adaptive governance. Recommendations are provided for integrating the proposed approach into existing cloud management platforms and DevOps pipelines to ensure long-term sustainability and return on investment.