Advertisement

The Survival Analysis of Big Data Application Over Auto-scaling Cloud Environment

  • R. S. RajputEmail author
  • Dinesh Goyal
  • Anjali Pant
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 985)

Abstract

The cloud resource provisioning is a mechanism of cloud resources allocation to cloud customers, and cloud customers have to interact with cloud resources using any cloud data center. The workload of the cloud environment consists of the significance of computing resources running situation in the cloud data centers. Cloud resource provisioning has a signification relation with cloud workload. The workload of cloud data centers is not the same all the time. For smooth and effective working of cloud resources at the cloud customer end, scaling of cloud resources required at cloud data center end. The scaling is a primary plan that to manage the extended work-load of the cloud data center. Scaling is implemented by adding additional or increasing computing power and memory capacity. Auto-scaling is one of an essential attribute of cloud computing that facilitates automatic provisioning of computing resources like add, remove, scale-up or scale-down resources depending upon workload. Big data applications associated with the large storage capacity and high processing power, cloud environment is suitable for fulfilling big data application requirement using auto-scaling of resources. In the present study, we have estimated the survival probability of auto-scaled cloud environment in the context of big data applications. Further, we investigated in this paper the importance of cloud resources that are used to build auto-scaling based cloud computing environment.

Keywords

Cloud computing Big data Survival analysis Auto-scaling Mean time to failure Importance of cloud resources 

References

  1. 1.
    Sosinsky, B.: Cloud Computing Bible. Wiley Publishing Inc., Indianapolis (2011)Google Scholar
  2. 2.
    Bills, D.: Fundamentals of Cloud Service Reliability. https://cloudblogs.microsoft.com/microsoftsecure/2014/03/24/reliability-series-1-reliability-vs-resilience/. Accessed 29 Nov 2018
  3. 3.
    Kaur, G., Kumar, R.: A review on reliability issues in cloud service. In: Proceeding of International Conference on Advancements in Engineering and Technology (ICAET 2015), pp. 9–13 (2015). Int. J. Comput. Appl.Google Scholar
  4. 4.
    Dui, H.: Reliability optimization of automatic control systems based on importance measures: a framework. Int. J. Performability Eng. 12(3), 297–300 (2016)Google Scholar
  5. 5.
    Abawajy, J.: What is workload (cloud data center service provisioning: theoretical and practical approaches). https://www.jnu.ac.in/content/LAB05/presentation/gian2018/day2.pdf. Accessed 9 Sept 2018
  6. 6.
    Rausand, M., Hayland, A.: System Reliability Theory Models, Statistical Methods, and Applications, 2nd edn. Wiley, Hoboken (2004)zbMATHGoogle Scholar
  7. 7.
    Adams, M.: An Introduction to designing reliable Cloud Services. https://chapters.cloudsecurityalliance.org/seattle/files/2013/08/An-Introduction. Accessed 8 Aug 2018
  8. 8.
    Sah, N., Singh, S.B., Rajput, R.S.: Stochastic analysis of a Web Server with different types of failure. J. Reliab. Stat. Stud. 3(1), 105–111 (2011)zbMATHGoogle Scholar
  9. 9.
    Yadav, N., Singh, V.B., Kumari, M.: Generalized reliability model for cloud computing. Int. J. Comput. Appl. 88(14), 13–16 (2014)Google Scholar
  10. 10.
    Nabeela, N.: All you need to know about cloud computing. http://eid100nujhatn.blogspot.in/2015/10/all-you-need-to-know-about-cloud.html. Accessed 20 Oct 2018
  11. 11.
    Rajput, R.S., Pant, A.: Optimal resource management in the cloud environment - a review. Int. J. Converging Technol. Manag. (IJCTM) 4(1), 12–24 (2018)Google Scholar
  12. 12.
    Rajput, R.S., Goyal, D., Singh, S.B.: Study of performance evolution of three-tier architecture based cloud computing system. In: Proceeding of Third International Conference on Internet of Things and Connected Technologies (ICIoTCT) (2018). http://dx.doi.org/10.2139/ssrn.3166719
  13. 13.
    Rajput, R.S., Goyal, D., Pant, A.: The survival analysis of three-tier architecture based cloud computing system. Int. J. Adv. Stud. Sci. Res. 3(11), 300–305 (2018). http://ssrn.com/abstract=3320440
  14. 14.
    RightScale Docs: Cloud Computing System Architecture Diagrams. http://docs.rightscale.com/cm/designers_guide/cm-cloud-computing-system-architecture-diagram.html. Accessed 20 Oct 2018
  15. 15.
    Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: Auto-scaling techniques for elastic applications in cloud environments. Technical report, Department of Computer Architecture and Technology University of the Basque Country (2012)Google Scholar
  16. 16.
    Arora, Y., Goyal, D.: Big data technologies: brief overview. Int. J. Comput. Appl. 131(9), 1–6 (2015)Google Scholar
  17. 17.
    Agarwal, B., Ramampiaro, H., Langseth, H., Ruocco, M.: A deep network model for paraphrase detection in short text messages. Inf. Process. Manag. 54(6), 922–937 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Centre of Cloud Infrastructure and SecuritySuresh Gyan Vihar UniversityJaipurIndia
  2. 2.Department of Computer EngineeringPoornima Institute of Engineering and TechnologyJaipurIndia
  3. 3.Government Polytechnic College, SankifarmSitarganjIndia

Personalised recommendations