DRAGON: Decentralized Fault Tolerance in Edge Federations
- URL: http://arxiv.org/abs/2208.07658v1
- Date: Tue, 16 Aug 2022 10:40:28 GMT
- Title: DRAGON: Decentralized Fault Tolerance in Edge Federations
- Authors: Shreshth Tuli and Giuliano Casale and Nicholas R. Jennings
- Abstract summary: We propose a novel memory-efficient deep learning based model, namely generative optimization networks (GON)
GONs use a single network to both discriminate input and generate samples, significantly reducing their memory footprint.
We propose a decentralized fault-tolerance method called DRAGON that runs simulations to quickly predict and optimize the performance of the edge federation.
- Score: 13.864161788250856
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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