To fully exploit the benefits of the fog environment, efficient management of data locality is crucial. Blind or reactive data replication falls short in harnessing the potential of fog computing, necessitating more advanced techniques for predicting where and when clients will connect. While spatial prediction has received considerable attention, temporal prediction remains understudied. Our paper addresses this gap by examining the advantages of incorporating temporal prediction into existing spatial prediction models. We also provide a comprehensive analysis of spatio-temporal prediction models, such as Deep Neural Networks and Markov models, in the context of predictive replication. We propose a novel model using Holt-Winter's Exponential Smoothing for temporal prediction, leveraging sequential and periodical user movement patterns. In a fog network simulation with real user trajectories our model achieves a 15% reduction in excess data with a marginal 1% decrease in data availability.

本文研究Fog computing环境中数据本地性的有效管理至关重要，传统的数据复制技术缺乏操作更高级别的技术，因此需要更先进的技术来预测客户何时何地连接，并借助Holt-Winter的指数平滑模型来将时间预测结合到现有的空间预测模型中，以提高数据可用性和减少数据冗余。

在边缘计算环境下预测复制运动的时间特征