# DRAGON: Decentralized Fault Tolerance in Edge Federations

arXiv: https://arxiv.org/abs/2208.07658 · HTML: https://mlsanity.com/paper/2208.07658/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 69 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 26 of 26)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Model compression via distillation and quantization](https://mlsanity.com/paper/1802.05668/) (2018)
- [TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2201.07284/) (2022)
- [ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning](https://mlsanity.com/paper/2006.00719/) (2020)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [Communication-Computation Trade-Off in Resource-Constrained Edge Inference](https://mlsanity.com/paper/2006.02166/) (2020)
- [GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework](https://mlsanity.com/paper/2008.11062/) (2020)
- [Partial Convolution based Padding](https://mlsanity.com/paper/1811.11718/) (2018)
- [COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments](https://mlsanity.com/paper/2104.14392/) (2021)
- [Self-supervised Adversarial Training](https://mlsanity.com/paper/1911.06470/) (2019)
- [Big Data Caching for Networking: Moving from Cloud to Edge](https://mlsanity.com/paper/1606.01581/) (2016)
- [Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: Evolution, vision, trends and open challenges](https://mlsanity.com/paper/1911.01941/) (2019)
- [PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing](https://mlsanity.com/paper/2112.02292/) (2021)
- [DeFog: Fog Computing Benchmarks](https://mlsanity.com/paper/1907.10890/) (2019)
- [Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking](https://mlsanity.com/paper/1908.01924/) (2019)
- [HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing](https://mlsanity.com/paper/2110.05529/) (2021)
- [Generative Optimization Networks for Memory Efficient Data Generation](https://mlsanity.com/paper/2110.02912/) (2021)
- [Dependability in Edge Computing](https://mlsanity.com/paper/1710.11222/) (2017)
- [Edge Federation: Towards an Integrated Service Provisioning Model](https://mlsanity.com/paper/1902.09055/) (2019)
- [CEFIoT: A Fault-Tolerant IoT Architecture for Edge and Cloud](https://mlsanity.com/paper/2001.08433/) (2020)
- [CAROL: Confidence-Aware Resilience Model for Edge Federations](https://mlsanity.com/paper/2203.07140/) (2022)
- [Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices](https://mlsanity.com/paper/2112.06263/) (2021)
- [Medusa: An Efficient Cloud Fault-Tolerant MapReduce](https://mlsanity.com/paper/1511.07185/) (2015)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers. A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrained devices. To address this challenge, we propose a novel memory-efficient deep learning based model, namely generative optimization networks (GON). Unlike GANs, GONs use a single network to both discriminate input and generate samples, significantly reducing their memory footprint. Leveraging the low memory footprint of GONs, we propose a decentralized fault-tolerance method called DRAGON that runs simulations (as per a digital modeling twin) to quickly predict and optimize the performance of the edge federation. Extensive experiments with real-world edge computing benchmarks on multiple Raspberry-Pi based federated edge configurations show that DRAGON can outperform the baseline methods in fault-detection and Quality of Service (QoS) metrics. Specifically, the proposed method gives higher F1 scores for fault-detection than the best deep learning (DL) method, while consuming lower memory than the heuristic methods. This allows for improvement in energy consumption, response time and service level agreement violations by up to 74, 63 and 82 percent, respectively.