Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments
This addresses efficient data management for fog computing, but it is incremental as it builds on existing spatial prediction models.
The paper tackled the problem of predicting when clients will connect in fog environments to improve data replication, achieving a 15% reduction in excess data with only a 1% decrease in data availability.
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.