AI-Driven Cloud Optimization: A Machine Learning Approach to Kubernetes Autoscaling in Financial Platforms - Chandrasekhar Anuganti, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
Cloud cost efficiency remains a critical concern for enterprises, especially in always-on Kubernetes environments. This paper details a cost optimization strategy using AI-driven intelligent autoscaling to dynamically provision compute nodes based on real-time workload demands. The solution incorporates usage telemetry, machine learning-based bin-packing algorithms, and custom eviction policies to balance performance and cost. Deployed in a high-availability, HIPAA-aligned payment integrity platform, the framework demonstrated monthly cloud savings of over 40%, while maintaining SLA adherence. This extended analysis provides comprehensive technical details of the ML algorithms, architectural components, implementation specifics, and performance characteristics. The approach serves as a replicable model for financial and healthcare institutions aiming to control operational costs without compromising reliability or compliance.
Keywords: Kubernetes; Cost Optimization; Intelligent Autoscaling; Financial Platforms; AI-Driven Solutions; Cloud Computing