DRAGON: Decentralized Fault Tolerance in Edge Federations
Open accessAbstract
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.
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DRAGON and CAROL emphasize detection accuracy and quality-of-service guarantees.
“predictive frameworks such as DRAGON [36] and CAROL [37] emphasize detection accuracy and quality-of-service guarantees”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [36]
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Neagu, M., Serban, C. M., Hangan, A., & Sebestyen, G. (2026) · Future Internet · doi:10.3390/fi18030137 · Cited by 3 · see citing works
- Authors
- Tuli, S., Casale, G., & Jennings, N. R.
- Affiliations
- Imperial College London (GB)
- Loughborough University (GB)
- Venue
- IEEE Transactions on Network and Service Management
- Published
- 2023-03
- Volume
- 20
- Issue
- 1
- Pages
- 276-291
- ISSN
- 1932-4537 (ISSN-L)
- 2373-7379
- OpenAlex
- W4293198951
- Cited by
- 21
- References
- 57
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 7 · 2023: 4
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Network and Service Management (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- President’s Ph.D. scholarship at Imperial College London
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Computer Networks and Communications › Distributed systems and fault tolerance
- Keywords
- memory-efficient deep learning, digital twin, fault detection, quality of service, service level agreement violation, edge computing, Raspberry Pi, energy consumption, response time
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