# Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices

arXiv: https://arxiv.org/abs/2510.06882 · HTML: https://mlsanity.com/paper/2510.06882/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 61 references that are in the map.
- First posted on arXiv 8 Oct 2025.

## Builds on (most cited first, 20 of 20)

- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [A survey of Bayesian Network structure learning](https://mlsanity.com/paper/2109.11415/) (2021)
- [FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices](https://mlsanity.com/paper/2008.08509/) (2020)
- [A Survey and Taxonomy of Self-Aware and Self-Adaptive Cloud Autoscaling Systems](https://mlsanity.com/paper/1609.03590/) (2016)
- [A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud](https://mlsanity.com/paper/2205.15795/) (2022)
- [Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices](https://mlsanity.com/paper/2212.12180/) (2022)
- [Vehicular Edge Computing and Networking: A Survey](https://mlsanity.com/paper/1908.06849/) (2019)
- [Equilibrium in the Computing Continuum through Active Inference](https://mlsanity.com/paper/2311.16769/) (2023)
- [Cloud, Fog or Edge: Where to Compute?](https://mlsanity.com/paper/2101.10417/) (2021)
- [Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System](https://mlsanity.com/paper/2101.06582/) (2021)
- [Designing Reconfigurable Intelligent Systems with Markov Blankets](https://mlsanity.com/paper/2311.10597/) (2023)
- [Urgent Edge Computing](https://mlsanity.com/paper/2404.13411/) (2024)
- [Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning](https://mlsanity.com/paper/2410.09099/) (2024)
- [DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments](https://mlsanity.com/paper/1810.04608/) (2018)
- [Tiny Autoscalers for Tiny Workloads: Dynamic CPU Allocation for Serverless Functions](https://mlsanity.com/paper/2203.00592/) (2022)
- [SLO-Aware Task Offloading within Collaborative Vehicle Platoons](https://mlsanity.com/paper/2409.17667/) (2024)
- [A Multidimensional Elasticity Framework for Adaptive Data Analytics Management in the Computing Continuum](https://mlsanity.com/paper/2501.11369/) (2025)
- [SLA Management in Intent-Driven Service Management Systems: A Taxonomy and Future Directions](https://mlsanity.com/paper/2208.01218/) (2022)
- [MACH: Multi-Agent Coordination for RSU-centric Handovers](https://mlsanity.com/paper/2505.07827/) (2025)

## Built on by (most cited first, 1 of 1)

- [Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI](https://mlsanity.com/paper/2607.20937/) (2026)

## Abstract (arXiv)

Edge devices have limited resources, which inevitably leads to situations where stream processing services cannot satisfy their needs. While existing autoscaling mechanisms focus entirely on resource scaling, Edge devices require alternative ways to sustain the Service Level Objectives (SLOs) of competing services. To address these issues, we introduce a Multi-dimensional Autoscaling Platform (MUDAP) that supports fine-grained vertical scaling across both service- and resource-level dimensions. MUDAP supports service-specific scaling tailored to available parameters, e.g., scale data quality or model size for a particular service. To optimize the execution across services, we present a scaling agent based on Regression Analysis of Structural Knowledge (RASK). The RASK agent efficiently explores the solution space and learns a continuous regression model of the processing environment for inferring optimal scaling actions. We compared our approach with two autoscalers, the Kubernetes VPA and a reinforcement learning agent, for scaling up to 9 services on a single Edge device. Our results showed that RASK can infer an accurate regression model in merely 20 iterations (i.e., observe 200s of processing). By increasingly adding elasticity dimensions, RASK sustained the highest request load with 28% less SLO violations, compared to baselines.