Přehled o publikaci
2019
IT Operations Analytics: Root Cause Analysis via Complex Event Processing
DRAŠAR, Martin and Tomáš JIRSÍKBasic information
Original name
IT Operations Analytics: Root Cause Analysis via Complex Event Processing
Authors
DRAŠAR, Martin (203 Czech Republic, guarantor, belonging to the institution) and Tomáš JIRSÍK (203 Czech Republic, belonging to the institution)
Edition
Washington DC, USA, 2019 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), p. 741-742, 2 pp. 2019
Publisher
IEEE
Other information
Language
English
Type of outcome
Proceedings paper
Country of publisher
United States of America
Confidentiality degree
is not subject to a state or trade secret
Publication form
electronic version available online
RIV identification code
RIV/00216224:14610/19:00108341
Organization
Ústav výpočetní techniky – Repository – Repository
ISBN
978-1-72810-618-2
ISSN
UT WoS
000469937200142
EID Scopus
2-s2.0-85067007496
Keywords in English
IT operation analysis; complex event processing; root cause; IP flows; Tesla; T-Rex
Links
TH02010185, research and development project.
Changed: 8/9/2020 01:03, RNDr. Daniel Jakubík
Abstract
In the original language
IT operation analytics (ITOA) is used for discovering complex patterns in data from IT systems. The analytics process still includes a significant portion of human interaction which makes the analysis costly and error-prone. Human operators need to formulate queries over the collected data to identify the complex patterns. Since the queries describe complex relations, the queries are usually multilevel, perplexing, and complicated to create. For the querying the complex relations, complex event processing methods are successfully used in other domains. In this paper, we demonstrate an application of the complex event processing principles in the ITOA domain. We adjust T-Rex complex event processing engine and improve TESLA event processing language to suit for ITOA tasks. Our demonstration includes two real-world use-cases. We show the utilization of the complex event processing for root cause analysis and demonstrate the natural formulation of complex queries that results in the reduction of the volume of the required human interaction.
