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。盲目的或被动的数据复制无法充分利用雾计算的潜力，需要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，填补了这一研究空白。我们还对时空预测模型（如深度神经网络和马尔可夫模型）在预测性复制场景下进行了全面分析。我们提出了一种基于霍尔特-温特指数平滑法的新模型，用于时间预测，该模型利用了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15%的冗余数据削减。