Cloud Monitoring Data Challenges: A Systematic Review

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9947)

Abstract

Organizations need to continuously monitor, source and process large amount of operational data for optimizing the cloud computing environment. The research problem is: what are cloud monitoring data challenges – in particular virtual CPU monitoring data? This paper adopts a Systematic Literature Review (SLR) approach to identify and report cloud monitoring data challenges. SLR approach was applied to initially identify a large set of 1861 papers. Finally, 24 of 1861 relevant papers were selected and reviewed to identify the five major challenges of cloud monitoring data: monitoring technology, virtualization technology, energy, availability and performance. The results of this review are expected to help researchers and practitioners to understand cloud computing data challenges and develop innovative techniques and strategies to deal with these challenges.

Keywords

Big data Cloud computing Capacity planning Monitoring And virtual CPU 

References

  1. 1.
    Alzoubi, Y.I., Gill, A.Q., Al-Ani, A.: Empirical studies of geographically distributed agile development communication challenges: a systematic review. Inf. Manag. 53(1), 22–37 (2016)CrossRefGoogle Scholar
  2. 2.
    GTI, What is Grounded Theory? (2008). http://www.groundedtheory.com/what-is-gt.aspx
  3. 3.
    Gill, A.Q., Bunker, D., Seltsikas, P.: Moving forward: emerging themes in financial services technologies’ adoption. Commun. Assoc. Inf. Syst., 36, 205–230 (2015)Google Scholar
  4. 4.
    Gill, A.Q.: Adaptive Cloud Enterprise Architecture. World Scientific, Singapore (2015)Google Scholar
  5. 5.
    Gill, A.Q., Bunker, D., Seltsikas, P.: An empirical analysis of cloud, mobile, social and green computing: financial services it strategy and enterprise architecture. In: 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing (DASC), pp. 697–704. IEEE (2011)Google Scholar
  6. 6.
    Jamail, N.S.M., Atan, R., Abdullah, R., Said, M.Y.: Development of SLA monitoring tools based on proposed DMI in cloud computing. Trans. Mach. Learn. Artif. Intell. 3(1), 01 (2015)Google Scholar
  7. 7.
    Kitchenham, B.A., Charters, S.: Procedures for performing systematic literature reviews in software engineering. Keele University & Durham University, UK (2007)Google Scholar
  8. 8.
    Kowall, J., Fletcher, C.: Modernize your monitoring strategy by combining unified monitoring and log analytics tools, (2014). Gartner http://www.gartner.com/document/code/257830?ref=grbody&refval=2809724
  9. 9.
    Meng, S., Liu, L.: Enhanced monitoring-as-a-service for effective cloud management. IEEE Trans. Comput. 62(9), 1705–1720 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    NIST: NIST Cloud Computing Reference Architecture (2011). http://www.nist.gov/customcf/get_pdf.cfm?pub_id=909505
  11. 11.
    Smith, S., Gill, A. Q., Hasan, H., Ghobadi, S.: An enterprise architecture driven approach to virtualisation. In: Proceedings of PACIS 2013 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of SoftwareUniversity of Technology SydneyUltimoAustralia

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