The proliferation of demanding applications and edge computing establishes
the need for an efficient management of the underlying computing
infrastructures, urging the providers to rethink their operational methods. In
this paper, we propose an Intelligent Proactive Fault Tolerance (IPFT) method
that leverages the edge resource usage predictions through Recurrent Neural
Networks (RNN). More specifically, we focus on the process-faults, which are
related with the inability of the infrastructure to provide Quality of Service
(QoS) in acceptable ranges due to the lack of processing power. In order to
tackle this challenge we propose a composite deep learning architecture that
predicts the resource usage metrics of the edge nodes and triggers proactive
node replications and task migration. Taking also into consideration that the
edge computing infrastructure is also highly dynamic and heterogeneous, we
propose an innovative Hybrid Bayesian Evolution Strategy (HBES) algorithm for
automated adaptation of the resource usage models. The proposed resource usage
prediction mechanism has been experimentally evaluated and compared with other
state of the art methods with significant improvements in terms of Root Mean
Squared Error (RMSE) and Mean Absolute Error (MAE). Additionally, the IPFT
mechanism that leverages the resource usage predictions has been evaluated in
an extensive simulation in CloudSim Plus and the results show significant
improvement compared to the reactive fault tolerance method in terms of
reliability and maintainability.

本文提出了一种利用循环神经网络（RNN）预测边缘资源使用情况和触发主动节点复制和任务迁移的智能主动容错（IPFT）方法，同时使用创新的混合贝叶斯进化策略（HBES）算法自动适应资源使用模型，实验结果表明该方法可显著提高可靠性和可维护性。

基于资源使用预测的边缘智能主动容错

Intelligent Proactive Fault Tolerance at the Edge through Resource Usage Prediction

Machine learning has recently gained traction as a way to overcome the slow
accelerator generation and implementation process on an FPGA. It can be used to
build performance and resource usage models that enable fast early-stage design
space exploration. First, training requires large amounts of data (features
extracted from design synthesis and implementation tools), which is
cost-inefficient because of the time-consuming accelerator design and
implementation process. Second, a model trained for a specific environment
cannot predict performance or resource usage for a new, unknown environment. In
a cloud system, renting a platform for data collection to build an ML model can
significantly increase the total-cost-ownership (TCO) of a system. Third,
ML-based models trained using a limited number of samples are prone to
overfitting. To overcome these limitations, we propose LEAPER, a transfer
learning-based approach for prediction of performance and resource usage in
FPGA-based systems. The key idea of LEAPER is to transfer an ML-based
performance and resource usage model trained for a low-end edge environment to
a new, high-end cloud environment to provide fast and accurate predictions for
accelerator implementation. Experimental results show that LEAPER (1) provides,
on average across six workloads and five FPGAs, 85% accuracy when we use our
transferred model for prediction in a cloud environment with 5-shot learning
and (2) reduces design-space exploration time for accelerator implementation on
an FPGA by 10x, from days to only a few hours.

LEAPER 是一种基于转移学习的方法，用于预测 FPGA 系统中的性能和资源使用率，通过将低端边缘环境训练的性能和资源使用率模型转移到新的高端云环境来提供快速准确的预测。实验结果表明，LEAPER 在云环境中使用传输模型进行预测时，在六种工作负载和五个 FPGA 的情况下，平均准确率为 85％，并且将 FPGA 加速器实现的设计空间探索时间从几天缩短为几个小时。