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AI-Driven Cloud Optimization: A Machine Learning Approach to Kubernetes Autoscaling in Financial Platforms

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  • Chandrasekhar Anuganti

Abstract

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.

Suggested Citation

  • Chandrasekhar Anuganti, 2024. "AI-Driven Cloud Optimization: A Machine Learning Approach to Kubernetes Autoscaling in Financial Platforms," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(2), pages 1151-1158, April.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i2:id:1753
    DOI: 10.32628/CSEIT24102152
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24102152
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