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。