集群工作负载分配：利用自然语言处理的语义软亲和力
Cluster Workload Allocation: Semantic Soft Affinity Using Natural Language Processing
AI总结：
本文提出利用自然语言处理实现语义软亲和力的集群工作负载分配方法，通过LLM提升调度准确性，验证了其在复杂场景中的有效性。
摘要：
集群工作负载分配通常需要复杂的配置，导致可用性缺口。本文介绍了一种基于自然语言处理的语义、意图驱动的调度范式，用于集群系统。该系统利用大型语言模型（LLM）通过Kubernetes调度器扩展器集成，以解释自然语言分配提示注释中的软亲和力偏好。开发了一个原型，包含集群状态缓存和意图分析器（使用AWS Bedrock）。实证评估显示，对于顶级模型如Amazon Nova Pro/Premier和Mistral Pixtral Large，LLM解析准确率超过95%（在评估基准数据集上的子集准确率）。在六个场景中的调度质量测试中，原型在复杂和量化场景中实现了优于或等同于标准Kubernetes配置的放置效果，并能处理冲突的软偏好。结果验证了使用LLM进行可访问调度的可行性，但也突显了同步LLM延迟的限制，建议在生产就绪中采用异步处理。本工作确认了语义软亲和力在简化工作负载编排中的可行性，并提出了一个概念验证设计。
英文摘要：
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 and presents a proof-of-concept design.
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