Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

An Efficient Approach for VM and Database Segmentation of Cloud Resources Over Cloud Computing

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Virtual machine (VM) clusters are used in cloud computing models to safeguard resources from failure and provide redundancy. Tasks for cloud users are planned by choosing appropriate resources to carry out the work within the virtual machine cluster. Issues with pre-configuration, downtime, complicated backup procedures, and disaster management plague the current VM clustering methods. High availability resources with dynamic and on-demand configuration are provided by virtual machine infrastructure. To improve efficiency and availability, the suggested technique supports the VM clustering process, which places and allocates virtual machines (VMs) based on the size of the requested task and the bandwidth level based on the migration, the suggested clustering procedure is divided into pre- and post-clustering stages. The task and bandwidth classification procedure groups jobs that can run in a virtual machine cluster with sufficient bandwidth. Depending on the VM’s availability inside the cluster, the bandwidth is mapped to the virtual machine. Several performance metrics, including bucket size, task execution time, VM lifetime, and VM utilization, are used in the VM clustering process. The suggested VM clustering’s primary goal is to map tasks to bandwidth-efficient virtual machines (VMs) in order to achieve high availability and dependability. As opposed to current algorithms, it shortens the time allotted for tasks and their execution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

The corresponding author can provide the datasets upon request.

References

  1. Subashini S, Kavitha V. A survey on security issues in service delivery models of cloud computing. J Netw Comput Appl. 2021;34(1):1–11.

    Article  Google Scholar 

  2. Kaur JP, Kaur R. Security issues and use of cryptography in cloud computing. Int J Adv Res Comput Sci Softw Eng. 2014. https://doi.org/10.32628/CSEIT206639.

    Article  Google Scholar 

  3. Wang L, Tao J, Kunze M. Scientific cloud computing: early definition and experience. In: Proceedings of the 10th IEEE international conference on high performance computing and communications, Austin, TX. 2008. pp. 825–830

  4. Reservoir work. http://www-03.ibm.com/press/us/en/pressrelease/23448.wss/. Accessed June 2008

  5. Amazon elastic compute cloud. http://aws.amazon.com/ec2. Accessed Nov 2007

  6. IBM blue cloud work. http://www3.ibm.com/press/us/en/pressrelease/22613.wss/, Accessed June 2008

  7. Nimbus work. http://workspace.globus.org/ clouds/nimbus.html/. Accessed June 2008

  8. Status work. http://www.acis.ufl.edu/vws/. Accessed June 2008

  9. OpenNEbula work. http://www.opennebula.org/. Accessed Apr 2008

  10. Sharma S, Bhushan B, Sharma S. Improvising information security in cloud computing environment. Inter J Comput Appl. 2014. https://doi.org/10.5120/15071-3503.

    Article  Google Scholar 

  11. DJ Bernstein, T Lange (editors). eBACS: ECRYPT Benchmarking of cryptographic systems. http://bench.crypto. Accessed Oct 2013

  12. Shanmugalakshmi M Prabu. Research issues on elliptic curve cryptography and its applications. IJCSNS Int J Comput Sci Netw Secur. 2009;9(6):19–22.

    Google Scholar 

  13. Wang H, Sheng B, Li Q. Elliptic curve cryptographybased access control in sensor networks. Int J Secur Netw. 2006;1(3/4):127–37.

    Article  Google Scholar 

  14. B Sharma, BPIT Rohini. Security architecture of cloud computing based on elliptic curve cryptography (ecc). ICETEM. 2013

  15. Wikipedia, the free encyclopedia of cloud computing

  16. Lim I, Coolidge E, Hourani P. Securing cloud and mobility: A Practitioner’s Guide. USA: CRC Press; 2013.

    Book  Google Scholar 

  17. https://www.techopedia.com/definition/30564/ellipticcurve-cryptography-ecc. Accessed 6 May 2024.

  18. Y Yu, L Xue, MH Au, W Susilo, J Ni, Y Zhang, AV Vasilakos, J Shen. Cloud data integrity checking with an identity-based auditing mechanism from RSA. https://wiki.openssl.org/index.php/Elliptic_Curve_Cryptography. Accessed 6 May 2024.

  19. Bhagawat VC, Arul A, Kumar LS. Survey on data security issues in cloud environment. Int J Innov Res Adv Eng. 2015. https://doi.org/10.4304/jsw.8.5.1068-1078.

    Article  Google Scholar 

  20. Harinath D, et al. Enhancing security through steganography by using sudoku puzzle and ECC algorithm. Int J Res Sci Eng Technol. 2015;2(6):29–43.

    Google Scholar 

  21. Harinath D, et al. Enhancing security by using ECC algorithm in wireless sensor networks. Int J Artif Intell Mechatron. 2015. https://doi.org/10.1109/ICECET58911.2023.10389443.

    Article  Google Scholar 

  22. Elliptic curve cryptography. https://en.wikipedia.org/wiki/Elliptic_curve_cryptograph RSA (algorithm). http://en.wikipedia.org/wiki/RSA_(algorithm). Accessed 6 May 2024.

  23. JavaTM cryptography extension (JCE), reference guide. http://docs.oracle.com/javase/1.5.0/docs/guide/security/jce/JCERefGuide.html. Accessed 6 May 2024.

Download references

Acknowledgements

The research work was made possible with the support of REVA University, Bangalore, India which provided the necessary facilities.

Funding

No funding received for this research.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, REVA University, Bangalore, India

    Sunil Manoli, Prabhuraj Metipatil & P. Raghavendra Nayaka

Authors
  1. Sunil Manoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prabhuraj Metipatil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Raghavendra Nayaka
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

This research was a collective effort, made possible through the collaboration and contributions of all authors involved.

Corresponding author

Correspondence to Sunil Manoli.

Ethics declarations

Conflicts of Interest

Hereby we are declaring that there is No Conflict of Interest related to this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manoli, S., Metipatil, P. & Raghavendra Nayaka, P. An Efficient Approach for VM and Database Segmentation of Cloud Resources Over Cloud Computing. SN COMPUT. SCI. 5, 977 (2024). https://doi.org/10.1007/s42979-024-03204-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-024-03204-6

Keywords

Search

Navigation