Cloud Cost Optimization with Spot and Reserved Instances

Abstract

Cloud computing offers flexible pricing options that can significantly reduce infrastructure costs when used intelligently. Among these, Amazon’s Reserved Instances (RIs) provide lower pricing in exchange for long-term commitment, while Spot Instances offer deeply discounted compute capacity with the trade-off of potential interruptions. This paper examines strategies to dynamically combine RIs and Spot Instances to optimize cost while maintaining performance and reliability. We propose an integrated model that leverages online algorithms and bidding strategies to balance cost savings and workload stability. Through simulations based on historical pricing data and realworld workload traces, our approach demonstrates substantial savings compared to static provisioning methods. The results indicate not only cost efficiencies but also resilience against price volatility and demand uncertainty. We conclude by discussing the practical implications for cloud consumers and suggesting paths for future enhancements, such as incorporating predictive analytics and hybrid cloud integration.