DRAGON: Decentralized Fault Tolerance in Edge Federations
This work addresses fault tolerance for edge computing systems, enabling more efficient deployment of AI applications in resource-constrained environments, though it appears incremental as it builds on existing deep learning and heuristic methods.
The paper tackles the challenge of deploying latency-critical and AI applications in edge federations by proposing DRAGON, a decentralized fault-tolerance method that improves fault-detection F1 scores and reduces memory usage, leading to up to 74%, 63%, and 82% improvements in energy consumption, response time, and service level agreement violations, respectively.
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.