System Failure Recognition and Identification by Analyzing Syslog and SNS Data: Applying Big Data Analysis to Network Operations
D. Vimala1, P. Nandhini2, R. Elankavi3
1D.Vimala, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
2P.Nandhini, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
3R. Elankavi, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
Manuscript received on 04 July 2019 | Revised Manuscript received on 17 July 2019 | Manuscript Published on 23 August 2019 | PP: 557-561 | Volume-8 Issue-9S3 August 2019 | Retrieval Number: I31090789S319/2019©BEIESP | DOI: 10.35940/ijitee.I3109.0789S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: We present two major information examination strategies for diagnosing the reasons for system disappointments and for identifying system disappointments early. Syslogs contain log information created by the framework. We dissected syslogs what’s more, prevailing with regards to distinguishing the reason for a system disappointment via consequently learning more than 100 million logs without requiring any past learning of log information. Investigation of the information of an interpersonal interaction benefit (in particular, Twitter) empowered us to recognize conceivable system disappointments by extricating system disappointment related tweets, which represent under 1% of all tweets, continuously and with high exactness
Keywords: Big Data, Syslog, Network Failure Detection.
Scope of the Article: Big Data Networking