Edge computing provides a cloud-like architecture where small-scale resources are distributed near the network edge, enabling applications on resource-constrained devices to offload latency-critical computations to these resources. While some recent work showed that the resource constraints of the edge could result in higher end-to-end latency under medium to high utilization due to higher queuing delays, to the best of our knowledge, there has not been any work on modeling the trade-offs of deploying on edge versus cloud infrastructures in the presence of mobility. Understanding the costs and trade-offs of this architecture is important for network designers, as the architecture is now adopted to be part of 5G and beyond networks in the form of the Multi-access Edge Computing (MEC). In this paper we focus on quantifying and estimating the cost of edge computing. Using closed-form queuing models, we explore the cost-performance trade-offs in the presence of different systems dynamics. We model how workload mobility and workload variations influence these trade- offs, and validate our results with realistic experiments and simulations. Finally, we discuss the practical implications for designing edge systems and developing algorithms for efficient resource and workload management.

翻译：边缘计算提供了一种类似云计算的架构，其中小规模资源分布在网络边缘附近，使得资源受限设备上的应用能够将延迟敏感的计算任务卸载到这些资源上。尽管近期一些研究表明，在中等至高利用率下，边缘资源的限制可能因更高的排队延迟而导致更高的端到端延迟，但据我们所知，目前尚未有研究对在移动性存在的情况下部署于边缘与云基础设施的权衡进行建模。理解该架构的成本与权衡对于网络设计者至关重要，因为该架构目前已被采纳为5G及未来网络的一部分，以多接入边缘计算（MEC）的形式存在。本文重点在于量化和估计边缘计算的成本。通过闭式排队模型，我们探讨了在不同系统动态下成本与性能的权衡。我们建模了工作负载移动性和工作负载变化如何影响这些权衡，并通过实际实验和仿真验证了我们的结果。最后，我们讨论了设计边缘系统以及开发高效资源与工作负载管理算法的实际意义。