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

arXiv: https://arxiv.org/abs/2506.10420 · HTML: https://mlsanity.com/paper/2506.10420/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Deep active inference agents using Monte-Carlo methods](https://mlsanity.com/paper/2006.04176/) (2020)
- [pymdp: A Python library for active inference in discrete state spaces](https://mlsanity.com/paper/2201.03904/) (2022)
- [Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices](https://mlsanity.com/paper/2212.12180/) (2022)
- [Equilibrium in the Computing Continuum through Active Inference](https://mlsanity.com/paper/2311.16769/) (2023)
- [Benchmarking Dynamic SLO Compliance in Distributed Computing Continuum Systems](https://mlsanity.com/paper/2503.03274/) (2025)
- [Towards smart and adaptive agents for active sensing on edge devices](https://mlsanity.com/paper/2501.06262/) (2025)
- [Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning](https://mlsanity.com/paper/2410.09099/) (2024)
- [Tiny Autoscalers for Tiny Workloads: Dynamic CPU Allocation for Serverless Functions](https://mlsanity.com/paper/2203.00592/) (2022)
- [Distributed Intelligence in the Computing Continuum with Active Inference](https://mlsanity.com/paper/2505.24618/) (2025)

## Abstract (arXiv)

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.