Cluster Workload Allocation: Semantic Soft Affinity Using Natural Language Processing
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.
6968de528bd7789bd70cc493
Leszek Sliwko
claimed_verified
2026-01-15T15:02:33.478Z
68c90f3b622d9314451ff9ff
https://cdn-avatars.huggingface.co/v1/production/uploads/68c90f3b622d9314451ff9ff/Xm00KSMoQQ2bef3c2Gbt7.jpeg
Leszek Sliwko
lsliwko
user
lsliwko
6968de528bd7789bd70cc494
Jolanta Mizeria-Pietraszko
Cluster Workload Allocation: Semantic Soft Affinity Using Natural Language Processing
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.
A semantic, intent-driven scheduling approach uses large language models to interpret natural language hints for workload allocation in cluster systems, achieving high accuracy and improved placement compared to traditional methods.
Large Language Model Kubernetes scheduler extender natural language processing intent-driven scheduling semantic soft affinity cluster workload allocation