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.

提出了基于生成优化网络（GON）的内存高效的深度学习模型，以及一个名为 DRAGON 的去中心化容错方法，用于优化边缘联合性能，实验结果显示，该方法在故障检测和服务质量方面优于基准方法，能够将能量消耗、响应时间和服务水平协议的违规率提高高达 74％、63％和 82％。