Leveraging Observability-Driven Predictive Analytics for Cost-Effective Hybrid Cloud Migration on AWS

Abstract

This article presents an innovative framework that revolutionizes hybrid cloud migration strategies by integrating advanced observability tools with predictive analytics capabilities on AWS infrastructure. The article introduces a comprehensive approach that combines real-time monitoring, machine learning-driven prediction models, and automated risk mitigation strategies to optimize migration success rates and reduce operational costs. Through extensive experimentation across multiple enterprise environments encompassing numerous virtual machines and applications, the framework demonstrated significant improvements in migration outcomes. The implementation of predictive monitoring resulted in a substantial reduction in unplanned downtime and high accuracy in failure prediction, while the cost optimization model achieved a meaningful reduction in overall infrastructure expenses. The article establishes a new paradigm for cloud migration by emphasizing proactive risk management and resource optimization through advanced observability techniques. This article provides valuable insights for organizations planning hybrid cloud migrations and establishes a foundation for future research in quantum-inspired optimization and cross-platform compatibility. The framework's success in addressing common migration challenges while maintaining scalability and adaptability makes it a significant contribution to the field of cloud computing and infrastructure management.