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.

翻译：为充分发挥雾环境的优势，高效管理数据局部性至关重要。盲目或反应式数据复制难以挖掘雾计算的潜力，亟需更先进的技术来预测客户端将何时何地进行连接。尽管空间预测已受到广泛关注，但时间预测仍研究不足。本文通过探讨将时间预测融入现有空间预测模型的优势来填补这一空白，同时深入分析了深度神经网络与马尔可夫模型等时空预测模型在预测式复制中的应用。我们提出了一种基于霍尔特-温特指数平滑法的新型时间预测模型，该模型利用用户移动的序列性和周期性模式。在采用真实用户轨迹的雾网络模拟中，该模型在数据可用性仅下降1%的前提下，实现了15%的冗余数据削减。