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Improved data-driven root cause analysis in fog computing environment

Improved data-driven root cause analysis in fog computing environment Internet of Things (IoT) and cloud computing are used in many real-time smart applications such as smart health-care, smart traffic, smart city, and smart industries. Fog computing has been introduced as an intermediate layer to reduce communication delay between cloud and IoT Devices. To improve performance of these smart applications, a predictive maintenance system needs to adopt anomaly detection and root cause analysis model that helps to resolve anomalies and avoid such anomalies in future. The state-of-art work on data-driven root cause analysis suffers from scalability, accuracy, and interpretability. In this paper, a multi-agent-based improved data-driven root cause analysis technique is introduced to identify anomalies and its root cause. Multiple agents are used to perform various operations like data collection, anomaly detection, and root cause analysis. The deep learning model LSTM autoencoder is used to find the anomalies, and a game theory approach called SHAP algorithm is used to find the root cause of the anomaly. The experiment is carried out in Google Colab with Keras Python library to evaluate the model. The evaluation result shows the improvement in accuracy and interpretability, as compared to state-of-the-art works. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Reliable Intelligent Environments Springer Journals

Improved data-driven root cause analysis in fog computing environment

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Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021
ISSN
2199-4668
eISSN
2199-4676
DOI
10.1007/s40860-021-00158-x
Publisher site
See Article on Publisher Site

Abstract

Internet of Things (IoT) and cloud computing are used in many real-time smart applications such as smart health-care, smart traffic, smart city, and smart industries. Fog computing has been introduced as an intermediate layer to reduce communication delay between cloud and IoT Devices. To improve performance of these smart applications, a predictive maintenance system needs to adopt anomaly detection and root cause analysis model that helps to resolve anomalies and avoid such anomalies in future. The state-of-art work on data-driven root cause analysis suffers from scalability, accuracy, and interpretability. In this paper, a multi-agent-based improved data-driven root cause analysis technique is introduced to identify anomalies and its root cause. Multiple agents are used to perform various operations like data collection, anomaly detection, and root cause analysis. The deep learning model LSTM autoencoder is used to find the anomalies, and a game theory approach called SHAP algorithm is used to find the root cause of the anomaly. The experiment is carried out in Google Colab with Keras Python library to evaluate the model. The evaluation result shows the improvement in accuracy and interpretability, as compared to state-of-the-art works.

Journal

Journal of Reliable Intelligent EnvironmentsSpringer Journals

Published: Oct 15, 2021

Keywords: Anomaly detection; Root cause analysis; LSTM; Autoencoder; SHAP; Fog computing

References