多维弹性调度让边缘服务自动调优，提升资源利用率。
Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods
- 用智能代理动态调整硬件与服务配置，实现多维弹性扩展。
- 四种代理均满足服务等级目标，深度强化学习收敛最快。
- 适合边缘计算、实时系统及资源受限场景的开发者参考。
边缘计算因资源严格受限，传统自动扩缩容方法难以适用，亟需更灵活的多维弹性策略。本文提出一种基于智能体的自动扩缩容框架，通过动态调节硬件资源与内部服务配置，在资源受限环境下最大化需求满足度。对比了四种类型代理：主动推理、深度Q网络、结构知识分析与深度主动推理，分别在并行运行的YOLOv8视觉识别与OpenCV二维码检测两个真实服务上测试。结果表明，所有代理均达到可接受的服务等级目标（SLO）性能，且收敛模式各异：深度Q网络得益于预训练，结构知识分析收敛迅速，深度主动推理兼具理论基础与实际可扩展性优势。研究验证了多维代理式扩缩容在边缘环境中的可行性，为未来研究提供支持。
原文摘要 · Abstract (English)
Edge computing breaks with traditional autoscaling due to strict resource constraints, thus, motivating more flexible scaling behaviors using multiple elasticity dimensions. This work introduces an agent-based autoscaling framework that dynamically adjusts both hardware resources and internal service configurations to maximize requirements fulfillment in constrained environments. We compare four types of scaling agents: Active Inference, Deep Q Network, Analysis of Structural Knowledge, and Deep Active Inference, using two real-world processing services running in parallel: YOLOv8 for visual recognition and OpenCV for QR code detection. Results show all agents achieve acceptable SLO performance with varying convergence patterns. While the Deep Q Network benefits from pre-training, the structural analysis converges quickly, and the deep active inference agent combines theoretical foundations with practical scalability advantages. Our findings provide evidence for the viability of multi-dimensional agent-based autoscaling for edge environments and encourage future work in this research direction.
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