Intelligent Spot Instance Management: AI-Driven Decision Framework for Cost-Optimized Cloud Computing

Authors: Hema Vamsi Nikhil Katakam

DOI: https://doi.org/10.37082/IJIRMPS.v13.i5.232827

Short DOI: https://doi.org/

Country: United States

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Abstract: Cloud computing offers elastic scalability, but on-demand instances are often expensive. Spot and preemptible instances from AWS and Azure provide significant cost advantages—up to 70%—but come with risks of sudden termination. This paper conceptually proposes an AI-driven framework for intelligent spot instance management that predicts instance interruptions, dynamically migrates workloads, and learns optimal bidding strategies. The framework integrates workload profiling, reinforcement learning, and predictive analytics to achieve cost efficiency without performance degradation. The proposed approach aims to balance economy and reliability, forming a foundation for sustainable, energy-efficient cloud infrastructure.

Keywords: Spot Instances, Predictive Scheduling, Resource Optimization, Workload Migration, , Cost Efficiency, Cloud Sustainability.

Paper Id: 232827

Published On: 2025-10-14

Published In: Volume 13, Issue 5, September-October 2025

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