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.

翻译：边缘计算因严格的资源限制而与传统自动伸缩方法不同，从而促使采用多种弹性维度实现更灵活的伸缩行为。本研究提出一种基于代理的自动伸缩框架，该框架能动态调整硬件资源和内部服务配置，以在受限环境中最大化需求满足度。我们比较了四种伸缩代理：主动推理、深度Q网络、结构知识分析以及深度主动推理，使用并行运行的两个实际处理服务进行测试：用于视觉识别的YOLOv8和用于二维码检测的OpenCV。结果表明，所有代理均能实现可接受的SLO性能，但收敛模式各不相同。深度Q网络受益于预训练，结构分析方法收敛迅速，而深度主动推理代理则结合了理论基础与实际可扩展性优势。我们的研究结果证明了基于代理的多维自动伸缩方法在边缘环境中的可行性，并鼓励未来在此研究方向开展进一步工作。