The vast data deluge at the network's edge is raising multiple challenges for the edge computing community. One of them is identifying edge storage servers where data from edge devices/sensors have to be stored to ensure low latency access services to emerging edge applications. Existing data placement algorithms mainly focus on locality, latency, and zoning to select edge storage servers under multiple environmental constraints. This paper uses a data placement framework to compare distance-based, latency-based, and spatial-awareness-based data placement strategies, which all share a decision-making system with similar constraints. Based on simulation experiments, we observed that the spatial-awareness-based strategy could provide a quality of service on par with the latency-based and better than the distance-based strategy.

翻译：边缘网络中海量数据的涌现给边缘计算领域带来了诸多挑战。其中之一是如何确定边缘设备/传感器数据应存储的边缘存储服务器，以确保新兴边缘应用的低延迟访问服务。现有数据放置算法主要侧重于局部性、延迟和分区，以便在多重环境约束下选择边缘存储服务器。本文采用一个数据放置框架，比较了基于距离、基于延迟和基于空间感知的三种数据放置策略，它们均共享一个具有相似约束的决策系统。基于仿真实验，我们观察到基于空间感知的策略能够提供与基于延迟策略相当的服务质量，并且优于基于距离的策略。