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.

翻译：多接入边缘计算（MEC）通过将计算能力部署至靠近移动终端的位置，有望支持时延敏感型应用。但我们在商用MEC部署中的实测数据表明，由于高尾延迟现象，导致频繁出现服务等级协议（SLO）违约。研究发现，无线接入网与边缘服务器的资源争用是根本原因，而缺乏SLO感知能力的调度器进一步加剧了该问题。现有SLO感知方案需依赖RAN-边缘协同机制，这种耦合架构使其难以实际部署——协同引入的通信延迟、对异构应用支持不足、以及对边缘资源争用的忽视，共同导致性能表现不佳。本文提出SMEC——一种实用化的SLO感知资源管理框架，通过在RAN与边缘服务器实现完全解耦的时分紧迫性调度操作，达成截止时间感知的资源编排。我们的核心洞察在于：标准5G协议规范与应用运行时特征天然蕴含可被SLO感知机制利用的信息，无需对基础设施或应用进行大规模改造。在自建5G MEC测试平台上的评估表明，SMEC可实现90-96%的SLO满足率（相较现有方案不足6%），并将尾延迟降低最高122倍。SMEC已开源发布于https://github.com/smec-project。