Big Data architecture for IT incident management

Rong Liu, Qicheng Li, Feng Li, Lijun Mei, Juhnyoung Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

IT incident management aims to restore normal service quality and availability of IT systems from interruptions. IT incidents often have complicated causes aggregated from an IT environment composed of thousands of interdependent components. Incident diagnosis then requires collecting and analyzing a large scale of data regarding these components, often, in real time to find suspect causes. It is extremely difficult to fulfill this requirement using traditional techniques. In this paper, we propose a new analysis architecture using Big Data techniques. This architecture leverages stream computing and MapReduce techniques to analyze data from various data sources, uses NoSQL databases to store incident-related documents and their relationships, and further utilizes other analytical techniques to examine the documents for root causes and failure prediction. We demonstrate this approach using a real-world example and present evaluation results from a recent pilot study.

Original languageEnglish
Title of host publicationProceedings of 2014 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2014
Pages424-429
Number of pages6
ISBN (Electronic)9781479960583
DOIs
StatePublished - 17 Nov 2014
Event2014 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2014 - Qingdao, China
Duration: 8 Oct 201410 Oct 2014

Publication series

NameProceedings of 2014 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2014

Conference

Conference2014 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2014
Country/TerritoryChina
CityQingdao
Period8/10/1410/10/14

Keywords

  • Big Data
  • Incident management
  • MapReduce
  • NoSQL
  • co-occurrence
  • reoccurrence
  • stream computing

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