Hybrid optimization for virtual machine migration of utilizing healthcare text in the cloud


Cloud computing as the modern technology that generates, processing, storing, and sharing of medical data has evolved significantly. The health industry has made a lot of progress in transforming its data management activities, from regular storage to the digitalization of health care data. Cloud computing impacting based on lowering of costs, availability of resources, and power. moreover patient has the right or ownership of data in-the-cloud virtualization technology. Keeping the data of the patient in the cloud also facilitates interoperability between the various sectors of the health-care sector-pharmacy, insurance, and payments. Cloud offers virtual hardware, runtime settings, and facilities for those with a credit card. Cloud infrastructure has become a common term for reference to various devices, resources, and concepts. The proposed framework provides a simulated migration approach that is complex and energy-intensive. By activating idle physical machinery mode, this mechanism reduces the power to conserve electricity. This study suggests a modern cloud-based Health care services paradigm for optimizing VM migration utilizing Parallel Particle Swarm Optimization (PPSO). To measure the performance of our VMs model, a new model for health care service is also provided. The findings reveal that, in the overall deployment period, the new model approaches 60% of the state-of-the-art implementations. Furthermore, device performance is increased by 6.2% for demanded data in real-time. Furthermore, the accuracy of the smart hybrid model of resource utilization is 96.8%. In all associated activities, the suggested model is 67% better than other referred versions.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119, 117–128.

    Article  Google Scholar 

  2. Almezeini, N., & Hafez, A. (2017). Task scheduling in cloud computing using lion optimization algorithm. Algorithms, 5, 7.

    Google Scholar 

  3. Arjun, Chitra, & Anto, Mr. (2015). Diagnosis of diabetes using support vector machine and ensemble learning approach. International Journal of Engineering and Applied Sciences, 2(11), 257790.

    Google Scholar 

  4. Bitam, S. (2012). Bees life algorithm for job scheduling in cloud computing. In Proceedings of the third international conference on communications and information technology, (pp. 186–191).

  5. Camati, R.S., Calsavara, A., & Lima Jr., L. (2014). Solving the virtual machine placement problem as a multiple multidimensional knapsack problem. ICN 2014, (p. 264).

  6. Chaurasia, N., Tapaswi, S., & Dhar, J. (2016). A pareto optimal approach for optimal selection of virtual machine for migration in cloud. International Journal of Computer Science and Information Security, 14(10), 117.

    Google Scholar 

  7. Chen, L., Zhang, J., Cai, L., Li, R., He, T., & Meng, T. (2015). Mtad: A multitarget heuristic algorithm for virtual machine placement. International Journal of Distributed Sensor Networks, 11(10), 679170.

    Google Scholar 

  8. Darwish, N. R., Mohamed, A. A., & Zohdy, B. S. M. (2016). Applying swarm optimization techniques to calculate execution time for software modules. IJARAI, 5(3), 12–17.

    Google Scholar 

  9. Fu, X., & Chen, Z. (2015). Virtual machine selection and placement for dynamic consolidation in cloud computing environment. Frontiers of Computer Science, 9(2), 322–330.

    MathSciNet  Article  Google Scholar 

  10. Hemalatha, M., et al. (2013). Cluster based bee algorithm for virtual machine placement in cloud data centre. Journal of Theoretical & Applied Information Technology, 57, 3.

    Google Scholar 

  11. Kumar, R., & Sahoo, G. (2014). Cloud computing simulation using cloudsim. arXiv:1403.3253.

  12. Mohana, S. J., Saroja, M., & Venkatachalam, M. (2014). Comparative analysis of swarm intelligence optimization techniques for cloud scheduling. International Journal of Innovative Science, Engineering & Technology, 1(10), 15–19.

    Google Scholar 

  13. Parikh, K., Hawanna, N., Haleema, P. K., Iyengar, N. C. S. N., et al. (2015). Virtual machine allocation policy in cloud computing using cloudsim in java. International Journal of Grid and Distributed Computing, 8(1), 145–158.

    Article  Google Scholar 

  14. Prathap, R. & Kaushik, S. A study of security challenges in federated cloud computing

  15. Prathap, R., & Mohanasundaram, R. (2018). Electronic health records (EHR) and cloud protection: The present problems. Journal of Computational and Theoretical Nanoscience, 15(6–7), 2227–2232.

    Article  Google Scholar 

  16. Prathap, R., Mohanasundaram, R., & Ashok Kumar, P. (2019). Design of EHR in cloud with security. In S. C. Satapathy, V. Bhateja, & S. Das (Eds.), Smart Intelligent Computing and Applications (pp. 419–425). New York: Springer.

    Google Scholar 

  17. Shrivastava, Anurag, Patel, Vaibhav, & Rajak, Sukanya. (2017). An energy efficient VM allocation using best fit decreasing minimum migration in cloud environment. International Journal of Engineering Science, 4076, 1–7.

    Google Scholar 

  18. Suseela, B. B. J., & Jeyakrishnan, V. (2014). A multi-objective hybrid ACO-PSO optimization algorithm for virtual machine placement in cloud computing. International Journal of Research in Engineering and Technology, 3(4), 474–476.

    Article  Google Scholar 

  19. Teyeb, H., Balma, A., Alouane, N., Ben, H., & Tata, S. (2014). Optimal virtual machine placement in large-scale cloud systems. In 2014 IEEE 7th international conference on cloud computing, (pp. 424–431). IEEE.

  20. Thiruvenkadam, T., & Kamalakkannan, P. (2016). Virtual machine placement and load rebalancing algorithm in cloud computing systems. IJESRT, 5(8), 346–359.

    Google Scholar 

  21. Zhao, J., Hu, L., Ding, Y., Xu, G., & Hu, M. (2014). A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment. PLoS ONE, 9(9), 1–9.

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to R. Prathap.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Prathap, R., Mohanasundaram, R. Hybrid optimization for virtual machine migration of utilizing healthcare text in the cloud. Int J Speech Technol (2021). https://doi.org/10.1007/s10772-021-09823-1

Download citation


  • Cloud computing
  • Healthcare service
  • Virtual machine migration
  • Parallel particle swarm optimization algorithm