Edge computing is a promising solution to enable low-latency IoT applications, by shifting computation from remote data centers to local devices, less powerful but closer to the end user devices. However, this creates the challenge on how to best assign clients to edge nodes offering compute capabilities. So far, two antithetical architectures are proposed: centralized resource orchestration or distributed overlay. In this work we explore a third way, called uncoordinated access, which consists in letting every device exploring multiple opportunities, to opportunistically embrace the heterogeneity of network and load conditions towards diverse edge nodes. In particular, our contribution is intended for emerging serverless IoT applications, which do not have a state on the edge nodes executing tasks. We model the proposed system as a set of M/M/1 queues and show that it achieves a smaller jitter delay than single edge node allocation. Furthermore, we compare uncoordinated access with state-of-the-art centralized and distributed alternatives in testbed experiments under more realistic conditions. Based on the results, our proposed approach, which requires a tiny fraction of the complexity of the alternatives in both the device and network components, is very effective in using the network resources, while incurring only a small penalty in terms of increased compute load and high percentiles of delay.

翻译：边缘计算通过将计算任务从远程数据中心转移至本地设备（尽管性能较弱但更接近终端用户设备），为实现低延迟物联网应用提供了一种前景广阔的解决方案。然而，这带来了如何最优地将客户端分配给提供计算能力的边缘节点的挑战。迄今为止，学界提出了两种对立的架构：集中式资源编排或分布式覆盖网络。本研究探索了第三种途径，称为无协调访问，其核心在于让每个设备探索多个机会，以机会主义方式适应不同边缘节点间网络与负载条件的异构性。特别地，我们的研究面向新兴的无服务器物联网应用，这类应用在执行任务的边缘节点上不保持状态。我们将所提出的系统建模为一组M/M/1队列，并证明其能实现比单边缘节点分配更小的抖动延迟。此外，我们在测试床实验中，于更现实的条件下将无协调访问与最先进的集中式和分布式替代方案进行了比较。结果表明，所提出的方法在设备和网络组件上仅需极低的复杂度，却能高效利用网络资源，同时仅带来计算负载增加和延迟高百分位数方面的微小代价。