# Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments

arXiv: https://arxiv.org/abs/2306.00575 · HTML: https://mlsanity.com/paper/2306.00575/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 92 references that are in the map.
- First posted on arXiv 1 Jun 2023.

## Builds on (most cited first, 17 of 17)

- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [STAN: Spatio-Temporal Attention Network for Next Location Recommendation](https://mlsanity.com/paper/2102.04095/) (2021)
- [Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks](https://mlsanity.com/paper/1705.08982/) (2017)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions](https://mlsanity.com/paper/2203.06852/) (2022)
- [Human Trajectory Prediction using Spatially aware Deep Attention Models](https://mlsanity.com/paper/1705.09436/) (2017)
- [T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction](https://mlsanity.com/paper/1611.07635/) (2016)
- [Towards Auction-Based Function Placement in Serverless Fog Platforms](https://mlsanity.com/paper/1912.06096/) (2019)
- [Managing Data Replication and Distribution in the Fog with FReD](https://mlsanity.com/paper/2303.05256/) (2023)
- [FBase: A Replication Service for Data-Intensive Fog Applications](https://mlsanity.com/paper/1912.03107/) (2019)
- [Towards Predictive Replica Placement for Distributed Data Stores in Fog Environments](https://mlsanity.com/paper/2108.10628/) (2021)
- [AuctionWhisk: Using an Auction-Inspired Approach for Function Placement in Serverless Fog Platforms](https://mlsanity.com/paper/2108.13222/) (2021)
- [Predictive Replica Placement for Mobile Users in Distributed Fog Data Stores with Client-Side Markov Models](https://mlsanity.com/paper/2111.03395/) (2021)

## Abstract (arXiv)

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.