Enrique Molina-Giménez

Javier Fabra, Enrique Molina-Giménez, Pedro García-López

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.

Taeyoon Kim, Kyumin Kim, Enrique Molina-Giménez et al.

Cloud users aim to minimize cost while maximizing performance by selecting the most suitable instance types for their workloads. To reduce expenses, spot instances have been widely adopted due to their steep discounts compared to on-demand pricing. However, their use introduces reliability risks due to potential interruptions, and existing research has primarily focused on mitigating this trade-off from a cost or availability perspective alone. Despite the diversity in hardware capabilities among instance types, current provisioning systems tend to ignore performance variation, selecting nodes solely based on minimum resource requirements. In this paper, we present KubePACS, a Kubernetes-native spot instance provisioning system that constructs node pools optimized for both cost and performance while guaranteeing high availability. KubePACS formulates the node selection process as a multi-objective optimization problem, incorporating real-time data such as spot prices, performance benchmarks, and availability scores, including the multi-node Spot Placement Score (SPS). It solves this problem efficiently using an Integer Linear Programming (ILP) approach guided by the Golden Section Search (GSS) algorithm to find the optimal configuration. By integrating with the Karpenter node autoscaler, KubePACS jointly optimizes instance-type selection and node scaling decisions within a standard provisioning workflow. KubePACS also adopts a novel heuristic to support workload-specific preferences by scaling performance metrics for specialized instances. Through extensive evaluation across synthetic and real-world workloads, KubePACS demonstrates on average 55.09% and up to 81.06% higher performance per dollar over state-of-the-art solutions such as Karpenter, SpotVerse, and SpotKube, which only reference the spot instance prices and limited availability data.