DRAGON: Decentralized fault tolerance in edge federations
File(s)
Accepted version
Author(s)
Tuli, Shreshth
Casale, Giuliano
Jennings, Nicholas R
Type
Journal Article
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.
Date Issued
2022-08-18
Date Acceptance
2022-08-15
Citation
IEEE Transactions on Network and Service Management, 2022, 20 (1), pp.276-291
ISSN
1932-4537
Publisher
Institute of Electrical and Electronics Engineers
Start Page
276
End Page
291
Journal / Book Title
IEEE Transactions on Network and Service Management
Volume
20
Issue
1
Copyright Statement
Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/9861704
Publication Status
Published
Date Publish Online
2022-08-18