Enabling SLO-Aware 5G Multi-Access Edge Computing with SMEC
This addresses latency-critical application performance for mobile users in 5G MEC deployments, offering a practical improvement over existing methods.
The paper tackled the problem of frequent SLO violations due to high tail latencies in 5G multi-access edge computing by introducing SMEC, a practical resource management framework that achieved 90-96% SLO satisfaction and reduced tail latency by up to 122x compared to under 6% for existing approaches.
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.