Proposed Methodology to Strengthen the Performance of Adaptive Cloud Using Efficient Resource Provisioning

  • Lata J. GadhaviEmail author
  • Madhuri D. Bhavsar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 755)


The delivery of services as a computing and management of the resources like CPU, memory, software, information, and devices for end users are the key responsibilities of cloud computing. To enable the services as per the demand of end users and provisioning the resources to its hosted applications are defined as an approach in this paper. The dynamic and complexity of cloud environment create some challenges in managing the resources to fulfill the need of fluctuating resources. For the commercial and scientific applications or jobs, resource management has to be managed as per their current requirement. Improving the runtime performance of adaptive cloud for cloud-based services using efficient resource provisioning strategy is the key terminology in this paper. Adaptive cloud is to be built to analyze process, classify, and manage the data for cloud-based services. To make the cloud more intelligent and to adapt the dynamic data analysis, it should be trained to accept the runtime need of end users.


  1. 1.
    Jiang, Y., Perng, C.-S., Li, T., Chang, R.: Self adaptive cloud capacity planning. In: Ninth International Conference on Service Computing, pp. 73–80. IEEE (2012)Google Scholar
  2. 2.
    Nagavaram, A., et al.: A cloud-based dynamic workflow for mass spectrometry data analysis. In: 2011 International Conference on Cloud Computing and Service Computing, eScience, 2011, pp. 219–226. IEEE (2011)Google Scholar
  3. 3.
    Buyya, R., Pandey, S., Vecchiola, C.: Cloudbus toolkit for market-oriented cloud computing. In: Proceedings of CloudCom Conference (2009)Google Scholar
  4. 4.
    Huo, J., Shang, S., Zhang, Z.: ACRUM: an adaptive cloud resource utilization model, pp. 34–46. IEEE (2013)Google Scholar
  5. 5.
    Wei, Y., Blake, M.B., Saleh, I.: Adaptive resource management for service workflows in cloud environments. In: 2013 IEEE 27th International Symposium on Parallel & Distributed Processing Workshops and Ph.D. Forum, 2013, pp. 2147–2156. IEEE (2013)Google Scholar
  6. 6.
    Iqbal, W., Matthew, N., Carrera, D., Janecek, P.: Adaptive resource provisioning for read intensive multi-tier applications in the cloud. Future Gen. Comput. Syst. 27(6), 871–894 (2011). SpringerGoogle Scholar
  7. 7.
    Zhang, Y., Huang, G., Liu, X., Mei, H.: Integrating resource consumption and allocation for infrastructure resources on-demand. In: 2010 IEEE 3rd International Conference on Cloud Computing, IEEE 2010, pp. 75–82 (2010)Google Scholar
  8. 8.
    Feng, G., Garg, S., Buyya, R., Li, W.: Revenue maximization using adaptive resource provisioning in cloud computing environment. In: 2012 ACM/IEEE 13th International Conference on Grid Computing, 2012 IEEE, pp. 192–200 (2012)Google Scholar
  9. 9.
    Wang, L., Duan, R., Li, X., Lu, S., Hung, T., Calheiros, R.N., Buyya, R.: An iterative optimization framework for adaptive workflow management in computational clouds. In: 11th IEEE International Symposium on Parallel and Distributed Processing With Applications (ISPA), Melbourne, Australia, 16–18 July (2013)Google Scholar
  10. 10.
    Gadhavi, L.J., Bhavsar, M.D.: Efficient and dynamic resource provisioning strategy for data processing using cloud computing. Int. Rev. Comput. Softw. (IRECOS) 11(8), 2016 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Institute of Technology, Nirma UniversityAhmedabadIndia

Personalised recommendations