🤖 AI Summary
This study addresses the challenges of unpredictable costs and single-region constraints associated with Spot instances in cloud services, which stem from dynamic regional pricing, variable resource availability, and interruption risks. To overcome these limitations, the authors propose an AI-driven, multi-region Spot fleet provisioning approach that integrates real-time monitoring with machine learning–based cost prediction models. Leveraging the AWS EC2 Spot Fleet API, the method enables accurate cross-region cost estimation and optimal resource allocation prior to deployment. As the first solution supporting both cross-region Spot cost forecasting and deployment optimization, this work transcends the inherent EC2 Spot restrictions of single-region operation and lack of cost predictability. Evaluated at a scale of 1,500 vCPUs, the approach achieves 99.79% cost prediction accuracy and realizes up to 64% cost savings by exploiting inter-regional price differentials.
📝 Abstract
Cloud service platforms increasingly rely on elastic infrastructures to support dynamic workloads. Spot instances provide discounted computing resources but introduce uncertainty due to dynamic pricing, resource availability, and interruption risks that vary across geographical regions. In Amazon Web Services, the EC2 Spot Service simplifies fleet provisioning through allocation strategies, but it cannot estimate fleet costs before deployment and restricts provisioning to a single region. This paper presents an AI-driven provisioning service for multi-region spot fleets. The proposed approach combines monitoring of provisioning plans with predictive models to estimate fleet configurations and prices before launch, enabling cost-aware deployment decisions across regions while preserving the operational behavior of the EC2 Spot Service. The system was validated with fleets of up to 1500 vCPUs. Experimental results show a prediction accuracy of 99.79% compared to the EC2 Spot Service and potential cost savings of up to 64% by exploiting regional price variability.