An Intelligent AIOps Framework for Automated Failure Detection and Recovery in Digital Workflows

Abstract

Failures in digital workflows can significantly disrupt business operations, leading to downtime, revenue loss, and reduced system reliability. This paper proposes an intelligent AIOps framework for automated failure detection and recovery in complex digital environments. The system integrates machine learning techniques with real-time monitoring, log analysis, and anomaly detection to identify system failures proactively. By leveraging predictive analytics and pattern recognition, the framework detects deviations in system behavior and triggers automated recovery mechanisms without human intervention. Experiments conducted on workflow datasets demonstrate improved accuracy, reduced mean time to detection (MTTD), and faster mean time to recovery (MTTR) compared to traditional rule-based monitoring systems. The model shows robustness across dynamic environments, ensuring consistent performance under varying workloads and system conditions. A user-friendly interface enables operators to monitor workflows, visualize anomalies, and receive real-time alerts and recovery actions. The core of the system is a machine learning-driven analytics engine that processes logs, metrics, and event data to identify failure patterns, enhancing system reliability and operational efficiency across cloud-based applications, microservices architectures, and enterprise IT systems.

Introduction

Modern systems face frequent failures due to increasing complexity, and traditional rule-based monitoring is inefficient, producing excessive alerts and requiring manual intervention. To address this, the proposed system uses machine learning and real-time data analysis to detect anomalies early and automate recovery.

The methodology involves collecting system data (logs, metrics, events), preprocessing it (cleaning, normalization, feature extraction), and applying algorithms like Isolation Forest, Random Forest, and LSTM to identify abnormal patterns. Once a failure is detected, automated actions such as restarting services, reallocating resources, or isolating faults are triggered.

The system follows an end-to-end pipeline including data acquisition, preprocessing, anomaly detection, and visualization through a graphical interface. Data enhancement techniques like balancing and augmentation improve model performance and robustness.

Results show that the framework accurately detects failures, classifies them (system, human, external errors), and significantly reduces detection and recovery time (MTTD & MTTR). Overall, the system improves reliability, minimizes downtime, and provides a scalable, intelligent solution for managing modern digital infrastructures.

Conclusion

The intelligent AIOps framework developed in this project provides a comprehensive solution for automated failure detection and recovery in digital workflows. The system integrates multiple components such as data collection, preprocessing, anomaly detection, root cause analysis, and automated recovery into a single platform. This integration allows the system to monitor operations continuously and respond to failures in real time. The implementation of machine learning techniques enables the system to learn from historical data and adapt to changing conditions. This makes the system more intelligent and capable of handling complex environments. The results obtained from testing show that the system achieves high accuracy in detecting anomalies and performs recovery actions efficiently.

References

[1] Deepali Arun Bhanage, Ambika Vishal Pawar, & Ketan Kotecha (2021). IT Infrastructure Anomaly Detection and Failure Handling: A Systematic Literature Review. IEEE Access. [2] Min Du et al. (2017). DeepLog: Anomaly Detectio and Diagnosis from System Logs using Deep Learning. ACM CCS. [3] Qiang Lin et al. (2016). Log Clustering based Problem Identification for Online Service Systems. ICSE. [4] Wei Xu et al. (2009). Detecting Large-Scale System Problems by Mining Console Logs. SOSP. [5] Haixun Wang et al. (2020). Machine Learning for IT Operations (AIOps): A Survey. IEEE Transactions.

Copyright

Copyright © 2026 M. Yazhini, S. Swathi, A. Akileshwari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.