面向云计算环境中LLM驱动文本分类的基于工作池编排的智能体自动扩缩容

中文摘要 AI 辅助

基于大语言模型（LLM）的系统在大规模文本处理中的日益普及，产生了对动态自动扩缩容的迫切需求，以管理高延迟、突发性和计算密集型工作负载。本文提出了一种通过工作池编排实现LLM驱动文本分类的智能体自动扩缩容框架。该框架集成了优先级任务队列、动态智能体工作池、实时指标收集器和应用层自动扩缩容器。其分类器无关的设计支持零样本和微调语言模型，而无需修改自动扩缩容逻辑。该框架使用Autoscaling+BART和Autoscaling+DeBERTa进行评估，并与静态分配以及独立的RoBERTa和DistilBERT基线进行比较。在AG News数据集上，Autoscaling+BART达到84.5%的准确率，而Autoscaling+DeBERTa将其提升至90.5%。在SMS Spam Collection数据集上，Autoscaling+DeBERTa达到99.5%的准确率，而Autoscaling+BART以较低的执行时间达到84.5%的准确率。总体而言，所提出的框架在资源效率方面始终优于基线方法，同时保持高分类性能，表明弹性工作池编排为云环境中可扩展的LLM驱动文本分类提供了一种有效且成本高效的解决方案。

英文摘要

The growing adoption of large language model (LLM)-based systems for large-scale text processing has created a critical need for dynamic autoscaling to manage high-latency, bursty, and computationally intensive workloads. This paper proposes an agentic autoscaling framework through worker-pool orchestration for LLM-driven text classification. The framework integrates a priority task queue, a dynamic pool of agent workers, a real-time metrics collector, and an application-layer autoscaler. Its classifier-agnostic design supports both zero-shot and fine-tuned language models without modifying the autoscaling logic. The framework is evaluated using Autoscaling+BART and Autoscaling+DeBERTa against static allocation and standalone RoBERTa and DistilBERT baselines. On the AG News dataset, Autoscaling+BART achieves 84.5% accuracy, while Autoscaling+DeBERTa improves it to 90.5%. On the SMS Spam Collection dataset, Autoscaling+DeBERTa achieves 99.5% accuracy, whereas Autoscaling+BART attains 84.5% accuracy with lower execution time. Overall, the proposed framework consistently outperforms the baseline approaches in resource efficiency while maintaining high classification performance, demonstrating that elastic worker-pool orchestration provides an effective and cost-efficient solution for scalable LLM-driven text classification in cloud environments.

发表机构

• The University of Melbourne（墨尔本大学）
• Banaras Hindu University（贝拿勒斯印度教大学）

机构由 AI 辅助整理，请以论文原文为准。