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

TL;DR

This paper compares four agent-based autoscaling methods for edge computing, demonstrating their effectiveness in dynamically adjusting resources and configurations to meet service requirements under resource constraints.

Contribution

It introduces a multi-dimensional agent-based autoscaling framework and evaluates four different agent types on real-world edge processing services.

Findings

All agents maintained acceptable SLO performance.

Deep Q Network benefits from pre-training.

Deep active inference combines theoretical and practical advantages.

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.