NDLI: Big Data architecture for IT incident management

Content Provider	IEEE Xplore Digital Library
Author	Rong Liu Qicheng Li Feng Li Lijun Mei Juhnyoung Lee
Copyright Year	2014
Description	Author affiliation: IBM Res. - China, Beijing, China (Qicheng Li; Feng Li; Lijun Mei) \|\| IBM T. J. Watson Res. Center, Yorktown Heights, NY, USA (Rong Liu; Juhnyoung Lee)
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.
Starting Page	424
Ending Page	429
File Size	577929
Page Count	6
File Format	PDF
ISBN	9781479960583
DOI	10.1109/SOLI.2014.6960762
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	co-occurrence Databases NoSQL Operating systems reoccurrence Big Data stream computing Incident management MapReduce
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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