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：为充分利用雾环境的优势，高效管理数据局部性至关重要。盲目或反应性的数据复制不足以发挥雾计算的潜力，这需要更先进的技术来预测客户端连接的位置和时间。尽管空间预测已受到广泛关注，但时间预测的研究仍显不足。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弥补这一空白。我们还在预测性复制背景下对深度神经网络和马尔可夫模型等时空预测模型进行了全面分析。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。在基于真实用户轨迹的雾网络仿真中，我们的模型在数据可用性仅下降1%的情况下，实现了冗余数据减少15%的效果。