Event Data Analysis in Large Virtualized Environment

  • M. B. Bharath
  • D. V. Ashoka
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)


Monitoring large virtualized datacenter is a daunting task. Around 3 to 3.5 million of events/alerts are received annually from 90+ customers, managed by our organization. Global command centers process these events in real-time and takes an appropriate necessary action for each of those alerts. One of the challenging tasks is to extract useful analytical details out of this large dataset. Attempts to run analytics on this event data poses a problem like heterogeneous source, unstructured data without any centralized repository to collect these events etc. These three issues are the same classic issues face by any “Big data” analysis. To address this issues a novel unified framework is built. This paper focus on big data problems and the solution proposed to address event data analysis in large virtualized environment as a use case. Along with this detailed design and implementation of the proposed solution with resulting report details are also discussed.


Big data Data analytics Virtualized datacenter Event monitoring Events reporting 


  1. 1.
    Gantz, J., Reinsel, D.: Extracting value from chaos. IDC iView 1142, 1–12 (2011)Google Scholar
  2. 2.
    Diebold, F.: On the origin(s) and development of the term “Big Data”. In: Pier Working Paper Archive 12-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania (2012)Google Scholar
  3. 3.
    Weiss, S.M., Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufman Publishers Inc, San Francisco (1998)zbMATHGoogle Scholar
  4. 4.
    Diebold, F.: “Big Data” dynamic factor models for macroeconomic measurement and forecasting. In: Discussion Read to the Eighth World Congress of the Econometric Society (2000)Google Scholar
  5. 5.
    Petland, A.: Reinventing society in the wake of big data. (2012).
  6. 6.
    Fayyad, U.: Big Data Analytics: Applications and Opportunities in On-line Predictive Modeling (2012).
  7. 7.
    Laney, D.: 3-D data management: controlling data volume, velocity and variety. META Group Res. Note 6, 70 (2001)Google Scholar
  8. 8.
  9. 9.
    Abhilash, C.B., Ashoka, D.V.: A survey on operating system virtualization methods and challenges. i-Manag. J. Inf. Technol. 5(1), 28–33 (2016)Google Scholar
  10. 10.
  11. 11.
    Aggarwal, C.C. (ed.): Managing and Mining Sensor Data. Advances in Database Systems. Springer, New York (2013). Scholar
  12. 12.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.EMC Software and Services India Pvt. LtdBangaloreIndia
  2. 2.JSS Academy of Technical EducationBangaloreIndia

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