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）旨在通过将计算能力部署在更靠近移动设备的位置，以支持对时延敏感的关键应用。然而，我们对商用MEC部署的测量显示，由于尾部时延过高，服务等级目标（SLO）违规频繁发生。我们确定无线接入网（RAN）和边缘服务器上的资源争用是根本原因，而缺乏SLO感知的调度器加剧了这一问题。现有的SLO感知方法需要RAN与边缘服务器之间的协同，这导致其部署不切实际，并且由于协同延迟、对异构应用支持有限以及忽视边缘资源争用等问题，性能表现不佳。本文提出了SMEC，一个实用且具备SLO感知能力的资源管理框架，它通过在RAN和边缘服务器端完全解耦的操作，实现了基于截止时间的调度。我们的核心见解是，标准的5G协议和应用行为本身就提供了可用于SLO感知管理的信息，而无需对基础设施或应用进行大规模改动。在我们5G MEC测试平台上的评估表明，SMEC实现了90-96%的SLO满足率，而现有方法的满足率低于6%，同时将尾部时延降低了高达122倍。我们已在https://github.com/smec-project开源SMEC。