🤖 AI Summary
This work proposes a semantic, intent-driven scheduling paradigm to address the limitations of existing cluster schedulers, which rely on complex configurations and struggle to express soft affinity preferences, leading to a usability gap. For the first time, large language models—such as Amazon Nova Pro/Premier and Mistral Pixtral Large—are integrated into a Kubernetes scheduler extension to interpret user scheduling intents expressed in natural language, enabling semantic soft affinity scheduling. The system leverages a cluster state cache and an AWS Bedrock–based intent analyzer. In evaluations, it achieves over 95% accuracy and matches or outperforms standard Kubernetes configurations across six scenarios, demonstrating particularly strong performance in complex, quantitative, and conflicting preference settings.
📝 Abstract
Cluster workload allocation often requires complex configurations, creating a usability gap. This paper introduces a semantic, intent-driven scheduling paradigm for cluster systems using Natural Language Processing. The system employs a Large Language Model (LLM) integrated via a Kubernetes scheduler extender to interpret natural language allocation hint annotations for soft affinity preferences. A prototype featuring a cluster state cache and an intent analyzer (using AWS Bedrock) was developed. Empirical evaluation demonstrated high LLM parsing accuracy (>95% Subset Accuracy on an evaluation ground-truth dataset) for top-tier models like Amazon Nova Pro/Premier and Mistral Pixtral Large, significantly outperforming a baseline engine. Scheduling quality tests across six scenarios showed the prototype achieved superior or equivalent placement compared to standard Kubernetes configurations, particularly excelling in complex and quantitative scenarios and handling conflicting soft preferences. The results validate using LLMs for accessible scheduling but highlight limitations like synchronous LLM latency, suggesting asynchronous processing for production readiness. This work confirms the viability of semantic soft affinity for simplifying workload orchestration.