Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices

📰 ArXiv cs.AI

MUDAP introduces multi-dimensional autoscaling for stream processing services on edge devices to sustain Service Level Objectives

advanced Published 30 Mar 2026
Action Steps
  1. Identify resource bottlenecks in edge devices
  2. Implement MUDAP for fine-grained vertical scaling
  3. Configure multi-dimensional autoscaling policies
  4. Monitor and adjust SLOs for competing services
Who Needs to Know This

DevOps and software engineering teams can benefit from MUDAP to efficiently manage edge device resources and ensure SLOs are met, while data scientists and AI engineers can utilize the platform to optimize stream processing services

Key Insight

💡 MUDAP enables efficient resource utilization and SLO satisfaction on edge devices through fine-grained autoscaling

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🚀 MUDAP: Multi-dimensional autoscaling for edge devices! 💻

Key Takeaways

MUDAP introduces multi-dimensional autoscaling for stream processing services on edge devices to sustain Service Level Objectives

Full Article

Title: Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices

Abstract:
arXiv:2510.06882v2 Announce Type: replace-cross 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 sc
Read full paper → ← Back to Reads

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