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:网络边缘的海量数据泛滥给边缘计算社区带来了多重挑战。其中之一是确定边缘存储服务器,以保证新兴边缘应用的低延迟访问服务中的设备/传感器数据的存储位置。现有数据放置算法主要集中于地理位置、延迟和分区,以在多重环境约束下选择边缘存储服务器。本文使用一个数据放置框架来比较基于距离、基于延迟和基于空间感知的数据放置策略,所有这些策略都共享一个类似约束的决策系统。基于模拟实验,我们观察到基于空间感知的策略可以提供与基于延迟相当的服务质量,优于基于距离的策略。