Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices

2025-10-08

AI Summary

Edge devices suffer from severe resource constraints, rendering existing autoscaling mechanisms inadequate for guaranteeing Service-Level Objectives (SLOs) in multi-stream processing. To address this, we propose MUDAP—a Multidimensional Adaptive Scaling Platform—that pioneers fine-grained, joint vertical scaling at both the service level (e.g., data quality, model size) and resource level. Its core innovation is an intelligent scaling agent based on Regression Analysis with Structural Knowledge (RASK), which constructs a continuous regression model of the processing environment to enable cross-dimensional co-optimization. Experimental evaluation demonstrates that RASK achieves high modeling accuracy within only 20 iterations and reduces SLO violation rates by 28% under peak load compared to state-of-the-art baselines. MUDAP significantly enhances service assurance and elastic efficiency in resource-constrained edge computing environments.

Technology Category

• Machine Learning: Scalability of ML Systems
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• Autoscaling stream processing on resource-limited edge devices
• Sustaining Service Level Objectives for competing edge services
• Optimizing multi-dimensional scaling across service and resource levels

Innovation

• Multi-dimensional autoscaling platform for edge devices
• Service-specific scaling of data quality and model size
• Regression analysis agent learns optimal scaling actions