AI-Driven Cloud Optimization: A Machine Learning Approach to Kubernetes Autoscaling in Financial Platforms
DOI:
https://doi.org/10.32628/CSEIT24102152Keywords:
Kubernetes, Cost Optimization, Intelligent Autoscaling, Financial Platforms, AI-Driven Solutions, Cloud ComputingAbstract
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.
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