DRAGON: Decentralized Fault Tolerance in Edge Federations

TL;DR

This paper introduces DRAGON, a decentralized fault-tolerance system for edge federations that uses low-memory deep learning models to predict and optimize system performance, significantly improving fault detection and QoS.

Contribution

The paper presents GON, a memory-efficient deep learning model, and DRAGON, a decentralized fault-tolerance method leveraging GON for real-time performance prediction in edge federations.

Findings

DRAGON outperforms baseline fault detection methods in F1 score.

DRAGON reduces memory usage compared to heuristic methods.

Energy consumption, response time, and SLA violations improved by up to 74%, 63%, and 82%.

Abstract

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.