# PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing

arXiv: https://arxiv.org/abs/2112.02292 · HTML: https://mlsanity.com/paper/2112.02292/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 4 Dec 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Multivariate Time-series Anomaly Detection via Graph Attention Network](https://mlsanity.com/paper/2009.02040/) (2020)
- [COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments](https://mlsanity.com/paper/2104.14392/) (2021)
- [Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks](https://mlsanity.com/paper/2009.02186/) (2020)
- [Resource Management in Fog/Edge Computing: A Survey](https://mlsanity.com/paper/1810.00305/) (2018)
- [Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges](https://mlsanity.com/paper/1911.01941/) (2019)
- [FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing](https://mlsanity.com/paper/1811.11978/) (2018)
- [DeFog: Fog Computing Benchmarks](https://mlsanity.com/paper/1907.10890/) (2019)
- [TANGO: Commonsense Generalization in Predicting Tool Interactions for Mobile Manipulators](https://mlsanity.com/paper/2105.04556/) (2021)
- [HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing](https://mlsanity.com/paper/2110.05529/) (2021)

## Built on by (most cited first, 8 of 8)

- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments](https://mlsanity.com/paper/2112.08916/) (2021)
- [MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems](https://mlsanity.com/paper/2112.07269/) (2021)
- [DRAGON: Decentralized Fault Tolerance in Edge Federations](https://mlsanity.com/paper/2208.07658/) (2022)
- [DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model](https://mlsanity.com/paper/2212.01302/) (2022)
- [CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments](https://mlsanity.com/paper/2302.05630/) (2023)
- [MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments](https://mlsanity.com/paper/2205.10642/) (2022)
- [FT-MoE: Sustainable-learning Mixture of Experts for Fault-Tolerant Computing](https://mlsanity.com/paper/2504.20446/) (2025)

## Abstract (arXiv)

Building a fault-tolerant edge system that can quickly react to node overloads or failures is challenging due to the unreliability of edge devices and the strict service deadlines of modern applications. Moreover, unnecessary task migrations can stress the system network, giving rise to the need for a smart and parsimonious failure recovery scheme. Prior approaches often fail to adapt to highly volatile workloads or accurately detect and diagnose faults for optimal remediation. There is thus a need for a robust and proactive fault-tolerance mechanism to meet service level objectives. In this work, we propose PreGAN, a composite AI model using a Generative Adversarial Network (GAN) to predict preemptive migration decisions for proactive fault-tolerance in containerized edge deployments. PreGAN uses co-simulations in tandem with a GAN to learn a few-shot anomaly classifier and proactively predict migration decisions for reliable computing. Extensive experiments on a Raspberry-Pi based edge environment show that PreGAN can outperform state-of-the-art baseline methods in fault-detection, diagnosis and classification, thus achieving high quality of service. PreGAN accomplishes this by 5.1% more accurate fault detection, higher diagnosis scores and 23.8% lower overheads compared to the best method among the considered baselines.