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Anomaly Detection and Levels of Automation for AI-Supported System Administration

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1070)

Abstract

Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating large IT infrastructures using AI-supported methods and tools on different levels. This includes automated anomaly detection and root cause analysis, remediation and optimization, as well as fully automated initiation of self-stabilizing activities. While the automation is mandatory due to the system complexity and the criticality of QoS-bounded responses, the measures compiled and deployed by the AI-controlled administration are not easily understandable or reproducible in all cases. Therefore, explainable actions taken by the automated systems are becoming a regulatory requirement for future IT infrastructures. In this paper we present a developed and deployed system named ZerOps as an example for the design of the corresponding architecture, tools, and methods. This system uses deep learning models and data analytics of monitoring data to detect and remediate anomalies.

Keywords

AIOps Predictive fault tolerance Anomaly detection 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.TU BerlinBerlinGermany

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