Enabling SLO-Aware 5G Multi-Access Edge Computing with SMEC

2026-01-27

AI Summary

This work addresses the frequent violation of service-level objectives (SLOs) for latency-sensitive applications in 5G multi-access edge computing (MEC), caused by resource contention between the radio access network (RAN) and edge servers. To resolve this, the paper proposes SMEC, a novel framework that leverages implicit information embedded in standard 5G protocols and application behaviors to enable fully decoupled, SLO-aware scheduling across the RAN and edge domains—without modifying existing protocols or applications and without requiring cross-domain coordination. By integrating tail-latency modeling with an SLO-driven resource allocation algorithm, SMEC achieves SLO compliance rates of 90–96% on a real-world 5G MEC platform, compared to less than 6% under baseline approaches, while reducing tail latency by up to 122×.

Technology Category

• Planning, Routing, and Scheduling: Scheduling
• Machine Learning: Learning on the Edge & Model Compression
• Multiagent Systems: Mechanism Design

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Multi-access edge computing (MEC) promises to enable latency-critical applications by bringing computational power closer to mobile devices, but our measurements on commercial MEC deployments reveal frequent SLO violations due to high tail latencies. We identify resource contention at the RAN and the edge server as the root cause, compounded by SLO-unaware schedulers. Existing SLO-aware approaches require RAN--edge coordination, making them impractical for deployment and prone to poor performance due to coordination delays, limited heterogeneous application support, and ignoring edge resource contention. This paper introduces SMEC, a practical, SLO-aware resource management framework that facilitates deadline-aware scheduling through fully decoupled operations at the RAN and edge servers. Our key insight is that standard 5G protocols and application behaviors naturally provide information exploitable for SLO-aware management without extensive infrastructure or application changes. Evaluation on our 5G MEC testbed shows that SMEC achieves 90-96% SLO satisfaction versus under 6% for existing approaches, while reducing tail latency by up to 122$\times$. We have open-sourced SMEC at https://github.com/smec-project.

Problem

• SLO violation
• multi-access edge computing
• tail latency
• resource contention
• 5G

Innovation

• SLO-aware
• multi-access edge computing
• 5G
• decoupled scheduling
• tail latency