Open AccessWay to Industry 4.0: Integrated IT monitoring with auto-identification of root cause for heterogeneous environment
Author(s) -
B. Ganesh Kumar,
Sahil Manchanda,
Senior Sales Engineer,
Deputy Manager
Publication year - 2020
Publication title -
international journal for research in engineering application and management
Language(s) - English
Resource type - Journals
ISSN - 2454-9150
DOI - 10.35291/2454-9150.2020.0400
Subject(s) - downtime , computer science , middleware (distributed applications) , automation , identification (biology) , root cause , scope (computer science) , isolation (microbiology) , legacy system , software deployment , computer security , product (mathematics) , software , software engineering , distributed computing , operating system , reliability engineering , engineering , mechanical engineering , botany , geometry , microbiology and biotechnology , mathematics , biology , programming language
For large enterprises driving the nation through their services or for systems addressing mission critical needs of the country even a bare minimum downtime of 0.1 % may prove fatal not just for the enterprise but also for the entire nation and hence must be strengthened with systems capable of detecting failure of components / sub-systems atan early stage. Industry 4.0 entails automation wherein systems visualize the entire operations and make decision autonomously. However, quick detection of faults followed by even faster isolation of the true cause is a longstanding challenge especially when one considers the count of endpoints employed (ranging from few hundred to several thousands) and the heterogeneity involved (be they physical servers or virtual, software or hardware, COTS / enterprise or embedded, applications or databases or middleware). No single product is available off the shelf which caters to such a wide scope in terms of monitoring. Moreover, implementation of different suites of products for monitoring different resources results in scattered, unrelated data which is meaningless unless huge manual effort is invested in analysis of the data to derive meaning out of it.