Fault Detection and Localization in Distributed Systems Using Recurrent Convolutional Neural Networks

Abstract

Early detection of faults is essential to maintaining the reliability of a distributed system. While there are many solutions for detecting faults, handling high dimensionality and uncertainty of system observations to make an accurate detection still remains a challenge. In this paper, we address this challenge with a two-dimensional convolutional neural network in the form of a denoising autoencoder with recurrent neural networks that performs simultaneous fault detection and diagnosis based on real-time system metrics from a given distributed system (e.g. CPU usage, memory consumption, etc.). The model provides a unified way to automatically learn useful features and make adaptive inferences regarding the onset of faults without hand-crafted feature extraction and human diagnostic expertise. In addition, we develop a Bayesian change-point detection approach for fault localization, in order to support the fault recovery process. We conducted extensive experiments in a real distributed environment over Amazon EC2 and the results demonstrate our proposal outperforms a variety of state-of-the-art machine learning algorithms that are used for fault detection and diagnosis in distributed systems.