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arXiv · 2506.10420

Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods

Boris Sedlak·Alireza Furutanpey·Zihang Wang·Víctor Casamayor Pujol·Schahram Dustdar
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Abstract

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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Boris Sedlak, Alireza Furutanpey, Zihang Wang, Víctor Casamayor Pujol, Schahram Dustdar. 2025-06-12. Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods. https://arxiv.org/abs/2506.10420

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