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％。

DRAGON: 边缘联盟中的分布式容错

DRAGON: Decentralized Fault Tolerance in Edge Federations

We introduce DRAGON, an open-source, fast and explainable hardware simulation
and optimization toolchain that enables hardware architects to simulate
hardware designs, and to optimize hardware designs to efficiently execute
workloads.
The DRAGON toolchain provides the following tools: Hardware Model Generator
(DGen), Hardware Simulator (DSim) and Hardware Optimizer (DOpt).
DSim provides the simulation of running algorithms (represented as data-flow
graphs) on hardware described. DGen describes the hardware in detail, with user
input architectures/technology (represented in a custom description language).
A novel methodology of gradient descent from the simulation allows us optimize
the hardware model (giving the directions for improvements in technology
parameters and design parameters), provided by Dopt.
DRAGON framework (DSim) is much faster than previously avaible works for
simulation, which is possible through performance-first code writing practices,
mathematical formulas for common computing operations to avoid cycle-accurate
simulation steps, efficient algorithms for mapping, and data-structure
representations for hardware state. DRAGON framework (Dopt) generates
performance optimized architectures for both AI and Non-AI Workloads, and
provides technology improvement directions for 100x-1000x better future
computing systems.

DRAGON 是一种开源的硬件仿真和优化工具链，提供了硬件模型生成器（DGen）、硬件仿真器（DSim）和硬件优化器（DOpt）。通过梯度下降的方法，DRAGON 框架可以优化硬件模型，提供性能优化的架构和技术改进方向，是一种适用于 AI 和非 AI 工作负载的快速硬件仿真和优化工具。