KubePACS: Kubernetes Cluster Using Performant, Highly Available, and Cost Efficient Spot Instances

2026-04-27

AI Summary

This work addresses the challenge of simultaneously optimizing cost, performance, and availability when deploying Kubernetes clusters on cloud spot instances. The authors propose KubePACS, the first system to formulate node selection as a multi-objective optimization problem that jointly considers real-time spot prices, instance performance benchmarks, and Spot Placement Scores. KubePACS incorporates a workload-aware performance scaling heuristic and solves the optimization using integer linear programming combined with golden-section search. Integrated with Karpenter for end-to-end scheduling, the system demonstrates significant improvements in cost efficiency: experiments show that KubePACS achieves an average performance-per-dollar gain of 55.09% over existing approaches, with peak improvements reaching 81.06%.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• spot instances
• Kubernetes
• cost-performance trade-off
• high availability
• node provisioning

Innovation

• multi-objective optimization
• spot instances
• Kubernetes autoscaling
• performance-per-dollar
• Integer Linear Programming