DeepFT：使用自监督深度代理模型的容错边缘计算
DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model
摘要：
延迟关键型AI应用的出现得到了边缘计算范式演变的支持。然而，边缘解决方案通常是资源受限的，由于对计算和通信能力的竞争加剧以及在过载条件下的错误应用行为，从而带来可靠性挑战。虽然可以挖掘大量生成的日志数据用于故障预测，但标记这些数据用于训练是一个手动过程，因此是自动化的限制因素。因此，许多公司采用无监督容错模型。然而，当这种故障模型需要适应非静态工作负载和不同的主机特性时，可能会导致准确性损失。为了解决这个问题，我们提出了一种新的建模方法，称为DeepFT，通过优化任务调度和迁移决策来主动避免系统过载及其不利影响。DeepFT使用深度代理模型来准确预测和诊断系统中的故障，并使用基于协同仿真的自监督学习来动态适应波动环境中的模型。它提供了一个高度可扩展的解决方案，因为模型大小的扩展仅为活动任务和主机数量每增加一个单位的3%和1%。在基于Raspberry-Pi的边缘集群上进行的大量实验表明，DeepFT在故障检测和QoS指标方面的表现优于最先进的基线方法。具体来说，DeepFT在故障检测方面给出了最高的F1分数，将服务期限违规减少了37%，同时将响应时间提高了9%。
英文摘要：
The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm. However, edge solutions are typically resource-constrained, posing reliability challenges due to heightened contention for compute and communication capacities and faulty application behavior in the presence of overload conditions. Although a large amount of generated log data can be mined for fault prediction, labeling this data for training is a manual process and thus a limiting factor for automation. Due to this, many companies resort to unsupervised fault-tolerance models. Yet, failure models of this kind can incur a loss of accuracy when they need to adapt to non-stationary workloads and diverse host characteristics. To cope with this, we propose a novel modeling approach, called DeepFT, to proactively avoid system overloads and their adverse effects by optimizing the task scheduling and migration decisions. DeepFT uses a deep surrogate model to accurately predict and diagnose faults in the system and co-simulation based self-supervised learning to dynamically adapt the model in volatile settings. It offers a highly scalable solution as the model size scales by only 3 and 1 percent per unit increase in the number of active tasks and hosts. Extensive experimentation on a Raspberry-Pi based edge cluster with DeFog benchmarks shows that DeepFT can outperform state-of-the-art baseline methods in fault-detection and QoS metrics. Specifically, DeepFT gives the highest F1 scores for fault-detection, reducing service deadline violations by up to 37\% while also improving response time by up to 9%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
